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- research-articleJuly 2024
IDGenRec: LLM-RecSys Alignment with Textual ID Learning
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 355–364https://doi.org/10.1145/3626772.3657821LLM-based Generative recommendation has attracted significant attention. However, in contrast to standard NLP tasks that inherently operate on human vocabulary, current generative recommendation approaches struggle to effectively encode items within the ...
- research-articleApril 2024JUST ACCEPTED
A Survey on Trustworthy Recommender Systems
- Yingqiang Ge,
- Shuchang Liu,
- Zuohui Fu,
- Juntao Tan,
- Zelong Li,
- Shuyuan Xu,
- Yunqi Li,
- Yikun Xian,
- Yongfeng Zhang
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead ...
- ArticleMarch 2024
GenRec: Large Language Model for Generative Recommendation
AbstractIn recent years, Large Language Models (LLMs) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm remains relatively ...
- research-articleMay 2024
OpenAGI: when LLM meets domain experts
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsArticle No.: 242, Pages 5539–5568Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents, enabling them to harness expert models for complex task-solving ...
- surveyOctober 2023
Fairness in Recommendation: Foundations, Methods, and Applications
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 14, Issue 5Article No.: 95, Pages 1–48https://doi.org/10.1145/3610302As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision-making. The satisfaction of users and the interests of platforms are closely related to the quality of the ...
- research-articleOctober 2023
Deconfounded Causal Collaborative Filtering
ACM Transactions on Recommender Systems (TORS), Volume 1, Issue 4Article No.: 17, Pages 1–25https://doi.org/10.1145/3606035Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each ...
- research-articleAugust 2023
A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 1Article No.: 29, Pages 1–29https://doi.org/10.1145/3605357State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models ...
- research-articleAugust 2023
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 2166–2176https://doi.org/10.1145/3580305.3599337This paper presents ExplainableFold (xFold), which is an Explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold (αFold) in this field, the underlying reasons for their predictions remain ...
- research-articleFebruary 2023
Counterfactual Collaborative Reasoning
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data MiningPages 249–257https://doi.org/10.1145/3539597.3570464Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two ...
- research-articleOctober 2022
Dynamic Causal Collaborative Filtering
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 2301–2310https://doi.org/10.1145/3511808.3557300Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating ...
- research-articleJuly 2022
Explainable Fairness in Recommendation
- Yingqiang Ge,
- Juntao Tan,
- Yan Zhu,
- Yinglong Xia,
- Jiebo Luo,
- Shuchang Liu,
- Zuohui Fu,
- Shijie Geng,
- Zelong Li,
- Yongfeng Zhang
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 681–691https://doi.org/10.1145/3477495.3531973Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model ...
- research-articleJune 2022
Causal factorization machine for robust recommendation
JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital LibrariesArticle No.: 10, Pages 1–9https://doi.org/10.1145/3529372.3530921Factorization Machines (FMs) are widely used for the collaborative recommendation because of their effectiveness and flexibility in feature interaction modeling. Previous FM-based works have claimed the importance of selecting useful features since ...
- research-articleApril 2022
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
WWW '22: Proceedings of the ACM Web Conference 2022Pages 1018–1027https://doi.org/10.1145/3485447.3511948Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is difficult to process and make use of the rich ...
- research-articleApril 2022
Path Language Modeling over Knowledge Graphsfor Explainable Recommendation
WWW '22: Proceedings of the ACM Web Conference 2022Pages 946–955https://doi.org/10.1145/3485447.3511937To facilitate human decisions with credible suggestions, personalized recommender systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain comprehensive information about ...
- research-articleOctober 2021
Counterfactual Explainable Recommendation
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 1784–1793https://doi.org/10.1145/3459637.3482420By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (...
- research-articleMay 2021
A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs
2021 IEEE International Conference on Robotics and Automation (ICRA)Pages 14061–14068https://doi.org/10.1109/ICRA48506.2021.9561271This paper presents a new self-supervised system for learning to detect novel and previously unseen categories of objects in images. The proposed system receives as input several unlabeled videos of scenes containing various objects. The frames of the ...
- research-articleMay 2021
Sample-level Data Selection for Federated Learning
IEEE INFOCOM 2021 - IEEE Conference on Computer CommunicationsPages 1–10https://doi.org/10.1109/INFOCOM42981.2021.9488723Federated learning (FL) enables participants to collaboratively construct a global machine learning model without sharing their local training data to the remote server. In FL systems, the selection of training samples has a significant impact on model ...
- research-articleJanuary 2020
Spatiotemporal Characteristics of Urban Surface Temperature and Its Relationship with Landscape Metrics and Vegetation Cover in Rapid Urbanization Region
Under the trend of rapid urbanization, the urban heat island (UHI) effect has become a hot issue for scholars to study. In order to better alleviate UHI effect, it is important to understand the effect of landuse/landcover (LULC) and landscape patterns on ...