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2025
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Oracle-MoE: Locality-preserving Routing in the Oracle Space for Memory-constrained Large Language Model Inference Link
Jixian Zhou, Fang Dong, Ruijun Huang, Hengjie Cao, Mengyi Chen, Yifeng Yang, Anrui Chen, Mingzhi Dong, Yujiang Wang, Dongsheng Li, David A. Clifton, Qin Lv, Rui Zhu, Chun Zhang, Fan Yang, Tun Lu, Ning Gu, Li Shang,
International Conference on Machine Learning (ICML), July 2025.
Abstract
Mixture-of-Experts (MoE) is widely adopted to deploy Large Language Models (LLMs) on edge devices with limited memory budgets. Although MoE is, in theory, an inborn memory-friendly architecture requiring only a few activated experts to reside in the memory for inference, current MoE architectures cannot effectively fulfill this advantage and will yield intolerable inference latencies of LLMs on memory-constrained devices. Our investigation pinpoints the essential cause as the remarkable temporal inconsistencies of inter-token expert activations, which generate overly frequent expert swapping demands dominating the latencies. To this end, we propose a novel MoE architecture, Oracle-MoE, to fulfill the real on-device potential of MoE-based LLMs. Oracle-MoE routes tokens in a highly compact space suggested by attention scores, termed the oracle space, to effectively maintain the semantic locality across consecutive tokens to reduce expert activation variations, eliminating massive swapping demands. Theoretical analysis proves that Oracle-MoE is bound to provide routing decisions with better semantic locality and, therefore, better expert activation consistencies. Experiments on the pretrained GPT-2 architectures of different sizes (200M, 350M, 790M, and 2B) and downstream tasks demonstrate that without compromising task performance, our Oracle-MoE has achieved state-of-the-art inference speeds across varying memory budgets, revealing its substantial potential for LLM deployments in industry.
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Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation Link
Chenyu Wang, Shuo Yan, Yixuan Chen, Yujiang Wang, Mingzhi Dong, Xiaochen Yang, Dongsheng Li, Robert P. Dick Qin, Lv Fan Yang, Tun Lu, Ning Gu, Li Shang
IEEE Transactions on Circuits and Systems for Video Technology, 2025.
Abstract
Denoising-based diffusion models have attained impressive image synthesis; however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://drmo-denoising-reuse.github.io.
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YouthCare: Building a Personalized Collaborative Video Censorship Tool to Support Parent-Child Joint Media Engagement Link
Wenxin Zhao, Fangyu Yu, Peng Zhang, Hansu Gu, Lin Wang, Siyuan Qiao, Tun Lu, Ning Gu
In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 1042, 1–20.
Abstract
To mitigate the negative impacts of online videos on teenagers, existing research and platforms have implemented various parental mediation mechanisms, such as Parent-Child Joint Media Engagement (JME). However, JME generally relies heavily on parents’ time, knowledge, and experience. To fill this gap, we aim to design an automatic tool to help parents/children censor videos more effectively and efficiently in JME. For this goal, we first conducted a formative study to identify the needs and expectations of teenagers and parents for such a system. Based on the findings, we designed YouthCare, a personalized collaborative video censorship tool that supports parents and children to collaboratively filter out inappropriate content and select appropriate content in JME. An evaluation with 10 parent-child pairs demonstrated YouthCare’s several strengths in supporting video censorship, while also highlighting some potential problems. These findings inspire us to propose several insights for the future design of parent-child collaborative JME systems.
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RemiHaven: Integrating "In-Town" and "Out-of-Town" Peers to Provide Personalized Reminiscence Support for Older Drifters Link
Xuechen Zhang, Changyang He, Peng Zhang, Hansu Gu, Ning Gu, Qi Shen, Zhan Hu, Tun Lu
In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25). Association for Computing Machinery, New York, NY, USA, Article 1034, 1–20.
Abstract
With increasing social mobility and an aging society, more older adults in China are migrating to new cities, known as “older drifters”. Due to fewer social connections and cultural adaptation, they face negative emotions such as loneliness and depression. While reminiscence-based interventions have been used to improve older adults’ psychological well-being, challenges such as the lack of tangible materials and limited social resources constrain the feasibility of traditional reminiscence approaches for older drifters. To address this challenge, we designed RemiHaven, a personalized reminiscence support tool based on a two-phase formative study. It integrates “In-Town” and “Out-of-Town” peer agents to enhance personalization, engagement, and emotional resonance in the reminiscence process powered by Multimodal Large Language Models (MLLMs). Our evaluations show RemiHaven’s strengths in supporting reminiscence while identifying potential challenges. We conclude by offering insights for the future design of reminiscence support tools for older migrants.
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Filtering Discomforting Recommendations with Large Language Models Link
Jiahao Liu, Yiyang Shao, Peng Zhang, Dongsheng Li, Hansu Gu, Chao Chen, Longzhi Du, Tun Lu, Ning Gu
In Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 3639–3650.
Abstract
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively identify and filter such content. To address this, we first conducted a formative study to understand users' practices and expectations regarding discomforting recommendation filtering. Then, we designed a Large Language Model (LLM)-based tool named DiscomfortFilter, which constructs an editable preference profile for a user and helps the user express filtering needs through conversation to mask discomforting preferences within the profile. Based on the edited profile, DiscomfortFilter facilitates the discomforting recommendations filtering in a plug-and-play manner, maintaining flexibility and transparency. The constructed preference profile improves LLM reasoning and simplifies user alignment, enabling a 3.8B open-source LLM to rival top commercial models in an offline proxy task. A one-week user study with 24 participants demonstrated the effectiveness of DiscomfortFilter, while also highlighting its potential impact on platform recommendation outcomes. We conclude by discussing the ongoing challenges, highlighting its relevance to broader research, assessing stakeholder impact, and outlining future research directions.
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Addressing popularity discrepancy in collaborative filtering Link
Cizhou Yu, Dongsheng Li, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu
Knowledge and Information Systems (2025).
Abstract
Collaborative filtering (CF) has emerged as the most successful type of recommendation algorithm during the past few decades. However, we observe that CF algorithms often exhibit a popularity discrepancy between user-interacted items and recommended items, e.g., CF algorithms may recommend items that are more popular than the ones the user preferred, especially to those who prefer non-popular items. To address this previously overlooked bias, we make three key contributions: (1) We introduce two novel metrics, PopDis_ED and PopDis_JS, to quantitatively measure popularity discrepancy, providing new perspectives beyond traditional bias indicators; (2) we propose an innovative model-agnostic mutual debiasing (MUDE) framework that uniquely combines a holistic model with a specialized long-tail model through a popularity-aware gating mechanism; (3) comprehensive experiments on four real-world datasets demonstrate that MUDE improves both recommendation accuracy and popularity discrepancy reduction, outperforming state-of-the-art debiasing methods. Moreover, MUDE shows strong generalizability across different types of CF algorithms, making it a practical solution for real-world recommender systems.
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FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation Link
Mingzhe Han, Dongsheng Li, Jiafeng Xia, Jiahao Liu, Hansu Gu, Peng Zhang, Ning Gu, Tun Lu
In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Abstract
Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.
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AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations Link
Jiahao Liu, Shengkang Gu, Dongsheng Li, Guangping Zhang, Mingzhe Han, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Abstract
LLM-based user agents, which simulate user interaction behavior, are emerging as a promising approach to enhancing recommender systems. In real-world scenarios, users' interactions often exhibit cross-domain characteristics and are influenced by others. However, the memory design in current methods causes user agents to introduce significant irrelevant information during decision-making in cross-domain scenarios and makes them unable to recognize the influence of other users’ interactions, such as popularity factors. To tackle this issue, we propose a dual-layer memory architecture combined with a two-step fusion mechanism. This design avoids irrelevant information during decisionmaking while ensuring effective integration of cross-domain preferences. We also introduce the concepts of interest groups and group-shared memory to better capture the influence of popularity factors on users with similar interests. Comprehensive experiments validate the effectiveness of AgentCF++. Our code is available at https://github.com/jhliu0807/AgentCF-plus.
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Improving LLM-powered Recommendations with Personalized Information Link
Jiahao Liu, Xueshuo Yan, Dongsheng Li, Guangping Zhang, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Abstract
Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline called CoT-Rec, which integrates two key Chain-of-Thought (CoT) processes -- user preference analysis and item perception analysis -- into LLM-powered recommendations, thereby enhancing the utilization of LLMs' reasoning abilities. CoT-Rec consists of two stages: (1) personalized information extraction, where user preferences and item perception are extracted, and (2) personalized information utilization, where this information is incorporated into the LLM-powered recommendation process. Experimental results demonstrate that CoT-Rec shows potential for improving LLM-powered recommendations. The implementation is publicly available at https://github.com/ jhliu0807/CoT-Rec.
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Unbiased Collaborative Filtering with Fair Sampling Link
Jiahao Liu, Dongsheng Li, Hansu Gu, Peng Zhang, Tun Lu, Li Shang, Ning Gu
In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Abstract
Recommender systems leverage extensive user interaction data to model preferences; however, directly modeling these data may introduce biases that disproportionately favor popular items. In this paper, we demonstrate that popularity bias arises from the influence of propensity factors during training. Building on this insight, we propose a fair sampling (FS) method that ensures each user and each item has an equal likelihood of being selected as both positive and negative instances, thereby mitigating the influence of propensity factors. The proposed FS method does not require estimating propensity scores, thus avoiding the risk of failing to fully eliminate popularity bias caused by estimation inaccuracies. Comprehensive experiments demonstrate that the proposed FS method achieves state-of-the-art performance in both point-wise and pairwise recommendation tasks. The code implementation is available at https://github.com/jhliu0807/Fair-Sampling.
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2024
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Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization Link
Shitong Duan, Xiaoyuan Yi, Peng Zhang, Yan Liu, Zheng Liu, Tun Lu, Xing Xie, Ning Gu
In Findings of the Association for Computational Linguistics: EMNLP 2024
Abstract
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. To steer LLMs towards human preference, alignment technologies have been introduced and gained increasing attention. Nevertheless, existing methods heavily rely on high-quality positive-negative training pairs, suffering from noisy positive responses that are barely distinguishable from negative ones. Given recent LLMs' proficiency in generating helpful responses, this work pivots towards a new research question: can we achieve alignment using solely human-annotated negative samples, preserving helpfulness while reducing harmfulness? For this purpose, we propose Distributional Dispreference Optimization (D2O), which maximizes the discrepancy between dispreferred responses and the generated non-negative ones. In this way, D2O effectively eschews harmful information without incorporating noisy positive samples, while avoiding collapse using self-generated responses as anchors. We demonstrate that D2O can be regarded as learning a distributional preference model reflecting human dispreference against negative responses, which is theoretically an upper bound of the instance-level DPO. Extensive experiments manifest that our method achieves comparable generation quality and surpasses the latest strong baselines in producing less harmful and more informative responses with better training stability and faster convergence.
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TSCompiler: efficient compilation framework for dynamic-shape models Link
Xiang Luo, Chen Zhang, Chenbo Geng, Yanzhi Yi, Jiahui Hu, Renwei Zhang, Zhen Zhang, Gianpietro Consolaro, Fan Yang, Tun Lu, Ning Gu & Li Shang
Science China Information Sciences, 2024, 67(10): 200403.
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Once Read is Enough: Finetuning-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge Link
Fang Dong, Mengyi Chen, Jixian Zhou, Yubin Shi, Yixuan Chen, Mingzhi Dong, Yujiang Wang, Dongsheng Li, Xiaochen Yang, Rui Zhu, Robert P. Dick, Qin Lv, Fan Yang, Tun Lu, Ning Gu, and Li Shang
in Proceedings Conference on Neural Information Processing Systems (NeurIPS), December 2024.
Abstract
Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, finetuning pretrained LMs is a common and indispensable practice. However, domain finetuning can be costly and time-consuming, hindering LMs’ deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and “long-tail” distributions as the leading cause for pretrained LMs’ inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model’s gradient updates and, consequently, are not effectively memorized, leading to poor domain specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently cluster domain knowledge together and assign long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, “outlier” clusters in an LM’s representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of finetuning-free language models.
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Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground Link
Zhenyu Xu, Hailin Xu, Zhouyang Lu, Yingying Zhao, Rui Zhu, Yujiang Wang, Mingzhi Dong, Yuhu Chang, Qin Lv, Robert P.Dick, Fan Yang, Tun Lu, Ning Gu, Li Shang
ACM Interactive Mobile Wearable & Ubiquitous Technologies (IMWUT), Vol. 8, No. 2, pp. 1-41, June 2024.
Abstract
Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their users. We define such essential knowledge as the common ground between chatbots and their users, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named OS-1, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate. We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. Our results show that by utilizing personal context, OS-1 progressively develops a better understanding of its users. This enhances user satisfaction and potentially leads to various personal service scenarios, such as emotional support and assistance.
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AOTree: Aspect Order Tree-based Model for Explainable Recommendation Link
Wenxin Zhao, Peng Zhang, Hansu Gu, Dongsheng Li, Tun Lu, Ning Gu
The International AAAI Conference on Web and Social Media (2024)
Abstract
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of content in reviews, such as words or aspects, and ignore the ordering relationship among them. This oversight neglects crucial ordering dimensions in the human decision-making process, leading to suboptimal performance. Therefore, in this paper, we propose Aspect Order Tree-based (AOTree) explainable recommendation method, inspired by the Order Effects Theory from cognitive and decision psychology, in order to capture the dependency relationships among decisive factors. We first validate the theory in the recommendation scenario by analyzing the reviews of the users. Then, according to the theory, the proposed AOTree expands the construction of the decision tree to capture aspect orders in users' decision-making processes, and use attention mechanisms to make predictions based on the aspect orders. Extensive experiments demonstrate our method's effectiveness on rating predictions, and our approach aligns more consistently with the user' s decision-making process by displaying explanations in a particular order, thereby enhancing interpretability.
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RAH! RecSys–Assistant–Human: A Human-Centered Recommendation Framework With LLM Agents Link
Yubo Shu, Haonan Zhang, Hansu Gu, Peng Zhang, Tun Lu, Dongsheng Li, Ning Gu
IEEE Transactions on Computational Social Systems (2024).
Abstract
The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human–computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems’ responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework’s efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.
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Simulating News Recommendation Ecosystems for Insights and Implications. Link
Guangping Zhang, Dongsheng Li, Hansu Gu, Tun Lu, Li Shang, Ning Gu
IEEE Transactions on Computational Social Systems (2024).
Abstract
Studying the evolution of online news communities is essential for improving the effectiveness of news recommender systems. Traditionally, this has been done through empirical research based on static data analysis. While this approach has yielded valuable insights for optimizing recommender system designs, it is limited by the lack of appropriate datasets and open platforms for controlled social experiments. This gap in the existing literature hinders a comprehensive understanding of the impact of recommender systems on the evolutionary process and its underlying mechanisms. As a result, suboptimal system designs may be developed that could negatively affect long-term utilities. In this work, we propose SimuLine, a simulation platform to dissect the evolution of news recommendation ecosystems and present a detailed analysis of the evolutionary process and underlying mechanisms. SimuLine first constructs a latent space well reflecting the human behaviors and then simulates the news recommendation ecosystem via agent-based modeling. Based on extensive simulation experiments and the comprehensive analysis framework consisting of quantitative metrics, visualization, and textual explanations, we analyze the characteristics of each evolutionary phase from the perspective of life-cycle theory and propose a relationship graph illustrating the key factors and affecting mechanisms. Furthermore, we explore the impacts of recommender system designing strategies, including the utilization of cold-start news, breaking news, and promotion, on the evolutionary process, which sheds new light on the design of recommender systems.
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Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning. Link
Shitong Duan, Xiaoyuan Yi, Peng Zhang, Tun Lu, Xing Xie, Ning Gu.
In Proceedings of the 12th International Conference on Learning Representations (ICLR), May 2024.
Abstract
This paper contains model outputs exhibiting unethical information. Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into automatically navigating LLMs’ ethical values based on value theories. Moving beyond static discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs’ value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, serving as an initial step in studying LLMs’ ethical values.
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Hierarchical Graph Signal Processing for Collaborative Filtering. Link
Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu.
Proceedings of the Web Conference (WWW), May 2024.
Abstract
Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for predicting user future interactions in recommender systems. However, current GSP methods recognize user interaction patterns based on the interactions of all users, so that the recognized interaction patterns are not fully user-matched and easily impacted by other users with different interaction behaviors, resulting in sub-optimal recommendation performance. To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. By linearly combining these two modules, HiGSP can recognize user-matched interaction patterns, so as to model user preference and predict user future interactions more accurately. Extensive experiments on six real-world datasets demonstrate the superiority of HiGSP compared to other GCN-based and GSP-based recommendation methods in terms of efficacy and efficiency.
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2023
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BNoteHelper: A Note-Based Outline Generation Tool for Structured Learning on Video Sharing Platforms. Link
Fangyu Yu, Peng Zhang, Xianghua Ding, Tun Lu, Ning Gu.
ACM Transactions on the Web (2023).
Abstract
Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video and aiming to consume the video itself. In this article, we design and implement BNoteHelper, a note-based video outline prototype that generates outline titles by extracting user-generated notes on Bilibili, using the BART model fine-tuned on a built dataset. As a browser plugin, BNoteHelper provides users with video overview and navigation as well as note-taking template, via two main features: outline table and navigation marker. The model and prototype are evaluated through automatic and human evaluations. The automatic evaluation reveals that, both before and after fine-tuning, the BART model outperforms T5-Pegasus in BLEU and Perplexity metrics. Also, the results from user feedback reveal that the generation outline sourced from notes is preferred by users over that sourced from video captions due to its more concise, clear, and accurate characteristics but also too general with less details and diversities sometimes. Two features of the video outline are also found to have respective advantages, especially in holistic and fine-grained aspects. Based on these results, we propose insights into designing a video summary from the user-generated creation perspective, customizing it based on video types, and strengthening the advantages of its different visual styles on video-sharing platforms.
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An Intent-based and Annotation-free Method for Duplicate Question Detection in CQA Forums. Link
Yubo Shu, Hansu Gu, Peng Zhang, Tun Lu, Ning Gu.
In The 2023 Conference on Empirical Methods in Natural Language Processing.
Abstract
With the advent of large language models (LLMs), Community Question Answering (CQA) forums offer well-curated questions and answers that can be utilized for instruction-tuning, effectively training LLMs to be aligned with human intents. However, the issue of duplicate questions arises as the volume of content within CQA continues to grow, posing a threat to content quality. Recent research highlights the benefits of detecting and eliminating duplicate content. It not only enhances the LLMs' ability to generalize across diverse intents but also improves the efficiency of training data utilization while addressing concerns related to information leakage. However, existing methods for detecting duplicate questions in CQA typically rely on generic text-pair matching models, overlooking the intent behind the questions. In this paper, we propose a novel intent-based duplication detector named Intent-DQD that comprehensively leverages intent information to address the problem of duplicate question detection in CQA. Intent-DQD first leverages the characteristics in CQA forums and extracts training labels to recognize and match intents without human annotation. Intent-DQD then effectively aggregates intent-level relations and establishes question-level relations to enable intent-aware duplication detection. Experimental results on fifteen distinct domains from both CQADupStack and Stack Overflow datasets demonstrate the effectiveness of Intent-DQD. Reproducible codes and datasets will be released upon publication of the paper.
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CL4CTR: A Contrastive Learning Framework for CTR Prediction. Link
Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu.
In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 805–813.
Abstract
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.
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AutoSeqRec: Autoencoder for Efficient Sequential Recommendation. Link
Sijia Liu, Jiahao Liu, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, Ning Gu.
In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 1493–1502.
Abstract
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.
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Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction. Link
Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang, and Ning Gu.
In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2523–2533.
Abstract
Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on https://github.com/anonctr/GDCN.
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JargonFM: A Framework With Multiple Interpretation Modes for Jargon Understanding in Online Communities. Link
Zhengqing Guan, Peng Zhang, Hansu Gu, Tun Lu, Baoxi Liu, Ning Gu.
IEEE Transactions on Computational Social Systems (2023).
Abstract
Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a jargon classifier, similar word identification based on a jargon synonyms selector, and representative text selection based on an example sentence selector. A jargon interpreter was also implemented to demonstrate the usage and usefulness of the interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.
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Personalized Graph Signal Processing for Collaborative Filtering. Link
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu.
In Proceedings of the ACM Web Conference 2023. 1264–1272.
Abstract
The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art CF methods and, as a nonparametric method, PGSP has very high training efficiency.
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Triple structural information modelling for accurate, explainable and interactive recommendation. Link
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Li Shang, Ning Gu.
In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1086–1095.
Abstract
In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information, resulting in suboptimal performance. To this end, we propose øurs, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. Specifically, øurs consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value ecomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs. Then, we fuse the predictions from the triple structural information sources to obtain the final recommendation results. By analyzing the relationship between the SVD-based and the recently emerging graph signal processing (GSP)-based collaborative filtering methods, we find that the essence of SVD is an ideal low-pass graph filter, so that the interest vector space in øurs can be extended to achieve explainable and interactive recommendation, making it possible for users to actively break through the information cocoons. Experiments on six public datasets demonstrated the effectiveness of øurs in accuracy, explainability and interactivity.
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2022
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Parameter-free Dynamic Graph Embedding for Link Prediction.
Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu.
Advances in Neural Information Processing Systems 35 (2022), 27623–27635.
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Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning.
Yutian Lu, Bo Wang, Yingying Zhao, Xiaochen Yang, Lizhe Li, Mingzhi Dong, Qin Lv, Fujian Zhou, Ning Gu, and Li Shang.
Energy 253 (2022), 124139.
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Exploring How Workspace Awareness Cues Affect Distributed Meeting Outcome.
Fangyu Yu, Peng Zhang*, Xianghua Ding, Tun Lu, Ning Gu.
International Journal of Human–Computer Interaction 39, 8 (2023), 1606–1625.
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Building Personalized Machine Translator based on User-generated Textual Content.
Peng Zhang, Zhengqing Guan, Baoxi Liu, Xianghua Ding, Tun Lu, Ning Gu.
The 2022 ACM Conference on Computer Supported Cooperative Work and Social Computing. (CCF A)
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Alignment work for urban accessibility: A study of how wheelchair users travel in urban spaces.
Yiying Wu, Xianghua Ding, Xuelan Dai, Peng Zhang*, Tun Lu, Ning Gu.
Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1–22.
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MCRF: Enhancing CTR Prediction Models via Multi-channel Feature Refinement Framework.
Fangye Wang, Hansu Gu, Dongsheng Li, Tun Lu, Peng Zhang*, Ning Gu.
In International Conference on Database Systems for Advanced Applications. Springer, 359–374.
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Jointly predicting future content in multiple social media sites based on multi-task learning.
Peng Zhang, Baoxi Liu, Tun Lu, Xianghua Ding, Hansu Gu, Ning Gu.
ACM Transactions on Information Systems (TOIS) 40, 4 (2022), 1–28.
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Floorplanning with graph attention.
Yiting Liu, Ziyi Ju, Zhengming Li, Mingzhi Dong, Hai Zhou, Jia Wang, Fan Yang, Xuan Zeng, Li Shang.
In Proceedings of the 59th ACM/IEEE Design Automation Conference. 1303–1308.
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Recursive disentanglement network.
Yixuan Chen, Yubin Shi, Dongsheng Li, Yujiang Wang, Mingzhi Dong, Yingying Zhao, Robert Dick, Qin Lv, Fan Yang, Li Shang.
In International Conference on Learning Representations.
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Cross-modal ambiguity learning for multimodal fake news detection.
Yixuan Chen, Dongsheng Li, Peng Zhang, Jie Sui, Qin Lv, Tun Lu, Li Shang.
In Proceedings of the ACM web conference 2022. 2897–2905.
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Do smart glasses dream of sentimental visions? Deep emotionship analysis for eyewear devices.
Yingying Zhao, Yuhu Chang, Yutian Lu, Mingzhi Dong, Yujiang Wang, Qin Lv, Robert P. Dick, Fan Yang, Tun Lu, Ning Gu, Li Shang.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 1 (2022), 1–29.
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Topology optimization of operational amplifier in continuous space via graph embedding.
Jialin Lu, Liangbo Lei, Fan Yang, Li Shang, Xuan Zeng.
In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 142–147.
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2021
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Jointly Predicting Future Content in Multiple Social Media Sites based on Multi-task Learning.
Peng Zhang, Baoxi Liu, Tun Lu, Xianghua Ding, Hansu Gu, Ning Gu.
ACM Transactions on Information Systems (TOIS) 40, 4 (2022), 1–28.
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Incremental graph convolutional network for collaborative filtering.
Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang and Ning Gu.
In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2170–2179.
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A Personalized Cross-Platform Post Style Transfer Method Based on Transformer and Bi-Attention Mechanism.
Zhuo Chen, Baoxi Liu, Peng Zhang*, Tun Lu, Ning Gu.
In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 85–93.
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Studying and understanding characteristics of post-syncing practice and goal in social network sites.
Peng Zhang, Baoxi Liu, Xianghua Ding, Tun Lu, Hansu Gu, Ning Gu.
ACM Transactions on the Web (TWEB) 15, 4 (2021), 1–26.
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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline.
Yingying Zhao, Mingzhi Dong, Yujiang Wang, Da Feng, Qin Lv, Robert P. Dick, Dongsheng Li, Tun Lu, Ning Gu, Li Shang.
IEEE Transactions on Multimedia 24 (2021), 2150–2163.
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MemX: An attention-aware smart eyewear system for personalized moment auto-capture.
Yuhu Chang, Yingying Zhao, Mingzhi Dong, Yujiang Wang, Yutian Lu, Qin Lv, Robert P. Dick, Tun Lu, Ning Gu, Li Shang.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2 (2021), 1–23.
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A high-frame-rate eye-tracking framework for mobile devices.
Yuhu Chang, Changyang He, Yingying Zhao, Tun Lu, Ning Gu.
In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1445–1449.
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Data engagement reconsidered: a study of automatic stress tracking technology in use.
Xianghua Ding, Shuhan Wei, Xinning Gui, Ning Gu, Peng Zhang.
In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–13.
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URIM: Utility-oriented role-centric incentive mechanism design for blockchain-based crowdsensing.
Zheng Xu, Chaofan Liu, Peng Zhang, Tun Lu, Ning Gu.
In Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III 26. Springer, 358–374.
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SANS: setwise attentional neural similarity method for few-shot recommendation.
Zhenghao Zhang, Tun Lu, Dongsheng Li, Peng Zhang, Hansu Gu, Ning Gu.
In Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III 26. Springer, 69–84.
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Neuse: A neural snapshot ensemble method for collaborative filtering.
Dongsheng Li, Haodong Liu, Chao Chen, Yingying Zhao, Stephen M. Chu, Bo Yang.
ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 6 (2021), 1–20.
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2020
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WikiChain: A Blockchain-Based Decentralized Wiki Framework.
Zheng Xu, Chaofan Liu, Peng Zhang*, Tun Lu, Ning Gu.
In Computer Supported Cooperative Work and Social Computing: 15th CCF Conference, ChineseCSCW 2020, Shenzhen, China, November 7–9, 2020, Revised Selected Papers 15. Springer, 46–57. (Best Paper Award)
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Collaborative fault detection for large-scale photovoltaic systems.
Yingying Zhao, Dongsheng Li, Tun Lu, Qin Lv, Ning Gu, Li Shang.
IEEE Transactions on Sustainable Energy 11, 4 (2020), 2745–2754.
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Getting the Healthcare We Want: The Use of Online" Ask the Doctor" Platforms in Practice.
Xianghua Ding, Xinning Gui, xiaojuan Ma, Zhaofei Ding, Yunan Chen.
In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13. (CHI2020 Honourable Mention Award)
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Exploring antecedents and consequences of toxicity in online discussions: A case study on reddit.
Yan Xia, Haiyi Zhu, Tun Lu, Peng Zhang, Ning Gu.
Proceedings of the ACM on Human-computer Interaction 4, CSCW2 (2020), 1–23.
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Understanding social interaction across social network sites.
Peng Zhang, Tun Lu, Baoxi Liu, Hansu Gu, Ning Gu.
International Journal of Human– Computer Interaction 36, 19 (2020), 1818–1833.
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A reliable cross-site user generated content modeling method based on topic model.
Baoxi Liu, Peng Zhang, Tun Lu, Ning Gu.
Knowledge-Based Systems 209 (2020), 106435.
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2016-2019
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Mixture Matrix Approximation for Collaborative Filtering.
Dongsheng Li, Chao Chen, Tun Lu*, Stephen M. Chu, Ning Gu.
IEEE Transactions on Knowledge and Data Engineering 33, 6 (2019), 2640–2653.
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How do you interact with your old friends on a new site：Understanding social ties among different social network sites.
Peng Zhang, Tun Lu, Hansu Gu, Xianghua Ding, Ning Gu.
The 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing, 2017: 2-9. (Best Paper Award)
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Effectiveness of Conflict Management Strategies in Peer Review Process of Online Collaboration Projects.
Wenjian Huang, Tun Lu, Haiyi Zhu, Guo Li, Ning Gu
In Proceedings of The 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW’16), San Francisco, USA.
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In Proceedings of The 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW’16), San Francisco, USA.
Peng Liu, Xianghua Ding, Ning Gu
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An algorithm for efficient privacy-preserving item-based collaborative filtering[J].
Dongsheng Li, Chao Chen, Qin Lv, Li Shang, Yingying Zhao, Tun Lu, Ning Gu.
Future Generation Computer Systems, 2016, 55: 311-320.
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SunForum: Understanding Depression in a Chinese Online Community.
Guo Li, Xiaomu Zhou, Tun Lu, Jiang Yang, Ning Gu
In Proceedings of The 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW’16), San Francisco, USA
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Group-based Latent Dirichlet Allocation (Group-LDA): Effectiveaudience detection for books in online social media
Peng Zhang，Hansu Gu，Mike Gartrell，Tun Lu，Dayi Yang，Xianghua Ding，Ning Gu
Knowledge-BasedSystems，2016.5.10，105：134~146
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Efficient privacy-preserving content recommendation for online social communities[J].
Dongsheng Li, Qin Lv, Li Shang, Ning Gu.
Neurocomputing, 2017, 219: 440-454.
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Mixture-Rank Matrix Approximation for Collaborative Filtering[C]
Dongsheng Li, Chao Chen, Wei Liu, Tun Lu, Ning Gu, Stephen M. Chu.
Advances in Neural Information Processing Systems. 2017: 477-485.
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Socially Embedded Work: A Study of Wheelchair Users Performing Online Crowd Work in China.
Xianghua Ding, Patrick C. Shih, Ning Gu.
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2017: 642-654.
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Understanding Relationship Overlapping on Social Network Sites: A Case Study of Weibo and Douban.
Peng Zhang, Haiyi Zhu, Tun Lu, Hansu Gu, Wenjian Huang, Ning Gu.
Proceedings of the ACM on Human-Computer Interaction, 2017, 1(CSCW): 1-18.
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AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering[C]
Dongsheng Li, Chao Chen, Qin Lv, Hansu Gu, Tun Lu, Li Shang, Ning Gu, Stephen M. Chu.
Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018: 741-751.
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Collaborative Filtering with Noisy Ratings[C].
Dongsheng Li, Chao Chen, Zhilin Gong, Tun Lu, Stephen Chu, Ning Gu.
Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2019: 747-755.
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Reading Health: Exploring Face Reading Technologies for Everyday Health[C]
Xianghua Ding, Yanqi Jiang, Xiankang Qin, Yunan Chen, Wenqiang Zhang, Lizhe Qi .
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 2019: 205.
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2011-2015
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Pistis: a privacy-preserving content recommender system for online social communities[C].
Dongsheng Li, Qin Lv, Huanhuan Xia, Li Shang, Tun Lu, Ning Gu.
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011 IEEE/WIC/ACM International Conference on. IEEE, 2011, 1: 79-86.
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YANA: an efficient privacy-preserving recommender system for online social communities[C].
Dongsheng Li, Qin Lv, Li Shang, Ning Gu.
Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011: 2269-2272.
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An Operational Transformation Based Synchronization Protocol for Web 2.0 Applications.
Bin Shao, Du Li, Tun Lu and Ning Gu.
In CSCW 2011: Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work(CSCW’11), Hangzhou, China, 2011.
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Back to the future: a hybrid approach to transparent sharing of video games over the internet in real time.
Sili Zhao, Du Li, Tun Lu, and Ning Gu.
In Proceedings of the ACM 2011 conference on Computer supported cooperative work (CSCW’11). ACM, Hangzhou, China, 187-196.
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Interest-based real-time content recommendation in online social communities[J].
Dongsheng Li, Qin Lv, Xing Xie, Li Shang, Huanhuan Xia, Tun Lu, Ning Gu.
Knowledge-Based Systems, 2012, 28: 1-12.
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Infrastructural experiences: an empirical study of an online arcade game platform in China.
Qi Wang, Xianghua Ding, Tun Lu, Huanhuan Xia, and Ning Gu.
In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW ’12). ACM, Seattle, WA, USA, 583-592.
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Digitality and Materiality of New Media: Online TV Watching in China.
Qi Wang, Xianghua Ding, Tun Lu, and Ning Gu.
In Proceedings of ACM Conference on Human Factors in Computing Systems (CHI’12). Austin, TX, USA, 347-356.
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AOPUT: A recommendation framework based on social activities and content interests[C] .
Yingying Deng, Tun Lu, Huanhuan Xia, Dongsheng Li, Tiejiang Liu, Xianghua Ding, Ning Gu.
Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference on. IEEE, 2013: 545-550.
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Meanings and Boundaries of Scientific Software Sharing.
Xing Huang, Xianghua Ding, Charlotte P.Lee, Tun Lu and Ning Gu.
In Proceedings of ACM Conference on Computer Supported Cooperative Work (CSCW ’13). San Antonio, TX, USA, 423-434.
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The Collective Infrastructural Work of Electricity: Exploring Feedback in a Prepay University Dorm in China.
Tengfei Liu, Xianghua Ding, Silvia Lindtner, Tun Lu, Ning Gu.
the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’13),Zurich, Switzerland,295-304.
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Item-based top-N recommendation resilient to aggregated information revelation[J].
Dongsheng Li, Qin Lv, Li Shang, Ning Gu.
Knowledge-Based Systems, 2014, 67: 290-304.
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A Partial Replication Approach for Anywhere Anytime Mobile Commenting.
Huanhuan Xia, Tun Lu, Bin Shao, Guo Li, Xianghua Ding, Ning Gu.
ACM 2014 Conference on Computer Supported Cooperative Work and Social Computing (CSCW’14), Baltimore, MD, USA, pages 530-541.
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Being senior and ICT: a study of seniors using ICT in China.
Yuling Sun, Xianghua Ding, Silvia Lindtner, Tun Lu, and Ning Gu.
In Proceedings of the 32nd annual ACM conference on Human factors in computing systems(CHI’14), Toronto, Canada, pages 3933-3942.
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Reliving the Past & Making a Harmonious Society Today: A Study of Elderly Electronic Hackers in China.
Yuling Sun, Silvia Lindtner, Xianghua Ding, Tun Lu and Ning Gu
In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 2015: 44-55.(CSCW 2015 Best Paper)
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Is It Good to Be Like Wikipedia?: Exploring the Trade-offs of Introducing Collaborative Editing Model to Q&A Sites.
Guo Li, Haiyi Zhu, Tun Lu, Xianghua Ding,Ning Gu
In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 2015: 1080-1091.
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2004-2010
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Normal forms for XML documents.
Teng Lv, Ning Gu, Ping Yan.
Information and Software Technology, Vol. 46, No. 12, pp. 839-846, 2004, Elsevier.
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Web Services Automatic Composition with Minimal Execution Price.
Jiamao Liu, Chenhui Fan, Ning Gu.
2005 IEEE International Conference of Web services (ICWS2005), 12-15 July, 2005, Orlando USA, pp. 302-309.
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Consistency maintenance based on the mark & retrace technique in groupware systems.
Ning Gu, Jiangming Yang, Qiwei Zhang.
In Proc. of 2005 International ACM SIGGROUP Conference on Supporting Group Work (GROUP’05), Sanibel Island, Florida, USA, Nov.6-9, 2005, pp. 264-273.
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A norm-driven state machine model for collaborative systems.
Shichao Zhang, Ning Gu, Jiangming Yang
Expert Systems with Applications, Vol. 31, No. 4, 2006, pp. 800-807, Elsevier.
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DCV: A Causality Detection Approach for Large-scale Dynamic Collaboration Environments.
Ning Gu, Qiwei Zhang Jiangming Yang, et al.
In Proc. of the 2007 International ACM Conference on Supporting Group Work (GROUP’07), Sanibel Island, USA, Nov 2007, pp. 157-166.
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PBmice: an integrated database system of piggyBac (PB) insertional mutations and their characterizations in mice.
Ling V. Sun, Ke Jin, Yiming Liu, Wenwei Yang, Xing Xie, Lin Ye, Li Wang, Lin Zhu, Sheng Ding, Yi Su, Jie Zhou, Min Han, Yuan Zhuang, Tian Xu, Xiaohui Wu, Ning Gu, Yang Zhong.
Nucleic Acids Research 36 (Database-Issue), pp. 729-734, 2008.
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Maintaining Time and Space Consistencies in Hybrid Engineering Environments: Framework and Algorithms.
Liping Gao, Bin Shao, Lin Zhu, Tun Lu, Ning Gu.
International Journal of Computers in Industry, Vol. 59, Issue 9, Dec. 2008, pp. 894-904, Elsevier.
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Lock-Free Consistency Control for Web2.0 Applicaitons.
Jiangming Yang, Haixun Wang, Ning Gu, Yiming Liu, Chunsong Wang, Qiwei Zhang.
In Proc. of the 17th International World Wide Web Conference (WWW’08), Beijing, China, April 21-25, 2008, pp. 725-734.
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An Approach to Sharing Legacy TV/Arcade Games for Real-Time Collaboration.
Sili Zhao, Du Li, Hansu Gu, Bin Shao, Ning Gu.
29th IEEE International Conference on Distributed Computing Systems, 2009 (ICDCS ’09), Montreal, Canada, June 22-26 ,2009, pp.165-172.
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A sequence transformation algorithm for supporting cooperative work on mobile devices.
Bin Shao, Du Li, and Ning Gu.
In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work (CSCW’10), Savannah, GA, USA, Feb.6-10, 2010, pp. 159 -168.(Best Paper Nomination)
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An Algorithm for Selective Undo of Any Operation in Collaborative Applications.
Bin Shao, Du Li, and Ning Gu.
In Group 2010: Proceedings of Group 2010 Conference(GROUP’10), Sanibel Island, FL., USA, Nov. 2010, pages 131-140.
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{{PastTitle}}
项目一：基于能耗私有云的绿色校园监管平台
该平台包括以下两个部分：
1、复旦大学节约型校园建筑节能监管平台
2、智能办公建筑节能管理系统OfficeEMS
针对高校用能使用情况缺乏实时监管以及因水电管网设备出现问题而造成的浪费等问题，2010年在国家住建部、教育部节约型校园项目的支持下，复旦大学计算机科学技术学院顾宁教授团队，基于私有云、大数据分析与预测、智能自动控制、社交网络、信息推荐、移动互联网等先进技术，自主研发了基于能耗私有云的绿色校园监管平台，该平台包括复旦大学节约型校园建筑节能监管平台和智能办公建筑节能管理系统OfficeEMS两部分。该平台可以通过PC端和Android、iOS移动端进行访问，利用部署的智能设备和构建的大规模云计算系统，对复旦大学水电等能耗大数据进行采集、传输、存储、统计、分析和预测，基于能耗规则和用能场景通过传感器和控制器对开关和设备的细粒度控制，结合构建的能耗社区及其相关社区服务，实现校园能耗的全方位监测、管理和控制。
该平台于2012年4月正式上线运行至今，共安装智能电表3790块，覆盖全校4个校区建筑共420栋；安装智能水表244块，覆盖全校4个校区建筑172栋；安装其他智能控制设备1637个，每天系统新产生220万条新纪录，现累计存储 3年14亿4千万多条能耗数据。
该平台可提供的核心服务功能包括：
1、能耗设备实时数据的采集、传输与存储：灯、空调等能耗设备运行状态、能耗数据的实时采集，环境如温湿度、光照度等信息采集、实时对智能设备进行控制，实时和离线能耗大数据的分布式云存储。
2、能耗大数据的处理、统计与预测：各楼宇、院系能耗数据统计、对比和分析，基于支持向量回归方法的细粒度建筑电能耗预测，基于混合高斯模型和EM算法的能耗分解预测，基于SA-DBSCAN算法的能耗异常检测。
3、基于用能行为分析的能耗社区服务：基于微信的能耗社区、面向用能场景的用户节能策略推送、基于局部敏感哈希技术的能耗社区资源实时推荐等。
项目二：基于智能交互控制设备的大数据残疾人自理与服务云平台
为了改善残疾人的行动不便、生活自理困难的现实状况，缓解残疾人的生理困难和心理压力，增加残疾人参与和融入社会的信心，由上海市残联支持，复旦大学计算机学院顾宁教授为核心的研发团队自主研发了基于智能交互控制设备的大数据残疾人自理与服务云平台。该平台通过部署和配备先进的语音、眼控等智能交互设备来辅助残疾人对日常生活中环境装置的无障碍控制，利用云平台技术对残疾人及其辅具使用行为大数据数据进行实时收集、处理和分析，在理解残疾人的行为模式的基础上提供可定制的个性化辅助服务，进而提高辅具使用的舒适性和有效性，同时通过众包、协同标注与信息推荐等前沿技术构建残疾人社区，帮助残疾人解决社交和就业的困难。
目前整个系统已在上海市杨浦区试点运行，智能化语音控制设备已在6户重症残疾人家庭中成功部署，得到了残疾人的良好反馈。首期工作得到了包括东方卫视、上海电视台新闻综合频道、凤凰网、人民网、中国日报、解放日报、光明日报、新华网、新浪科技等多家知名媒体报道。系统研发团队还应上海市残联邀请参加了2015年国际健康生活产业暨康复无障碍博览会，在博览会期间中国残联、中国肢残协会、上海市残联、北京市残联、湖北省残联、广东省残联和台湾无障碍协会等残疾人事业团体的主要领导、复旦大学副校长林尚立教授、残疾人代表“中国第一女赛车手”吴霞、多家企业及生产商负责人以及数十位残疾人观展代表先后到实验室展区观看了平台演示，并对该平台以及实验室从事的相关工作给予了高度评价。计划在今年年底在杨浦区部署23户重症残疾人家庭，明年计划部署100户残疾人家庭。
该平台可提供的核心服务功能包括：
1、基于智能交互设备的残疾人自理服务：通过部署自主研发的硬件设备，以语音、眼控等方式方便残疾人控制常见的家居设备，并对其提供提醒、备忘服务。
2、基于大数据的残疾人行为及辅具监管服务：通过对残疾人日常行为大数据进行采集，并分析其变化模式及规律得到残疾人的行为模型，为残疾人提供个性化的定制服务；利用蓝牙0和GPS技术定位跟踪辅具，对辅具器械的使用方法和状态大数据进行分析，及时发现和预警异常情况等。
3、残疾人互助社区服务：基于位置信息的社区移动微互助服务；方便残疾人出行的残疾人无障碍设施协同标注服务；基于微信开发的残疾人信息分享交流平台，根据社区中的志愿者及残疾人相关大数据分析建立推荐模型，实现智能化推荐志愿者服务。
4、面向残疾人网络就业的大数据协同标注服务：将大数据人工标注任务分解成众包的微任务模块，提供面向残疾人的交互辅助手段和协同互助方式参与完成大数据的标注微任务，解决残疾人网络就业问题。
附部分媒体报道链接：
凤凰网：
http://news.ifeng.com/a/20150412/43532746_0.shtml
人民网：
http://tv.people.com.cn/n/2015/0402/c39805-26792196.html
中国日报：
http://www.chinadaily.com.cn/hqcj/xfly/2015-04-07/content_13500949.html
光明日报：
http://epaper.gmw.cn/gmrb/html/2015-04/12/nw.D110000gmrb_20150412_10-04.htm
新华网：
http://news.xinhuanet.com/newmedia/2015-04/07/c_134129272.htm
新浪科技：
http://tech.sina.com.cn/i/2015-04-06/doc-iavxeafs4635354.shtml
复旦大学：
http://news.fudan.edu.cn/2015/0407/38509.html
复旦大学计算机科学技术学院：
http://www.cs.fudan.edu.cn/?p=12755
