76 citations · 282 across the 19 of their papers we have counts for
12 papers · 1 filter
IDGenRec: LLM-RecSys Alignment with Textual ID Learning
Juntao Tan, Shuyuan Xu, Wenyue Hua +3
Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However…
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
Zelong Li, Jianchao Ji, Yingqiang Ge +2
Recently emerged prompt-based Recommendation Language Models (RLM) can solve multiple recommendation tasks uniformly. The RLMs make full use of the inherited knowledge learned from…
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Juntao Tan, Yingqiang Ge, Yan Zhu +4
Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminish…
GenRec: Large Language Model for Generative Recommendation
Jianchao Ji, Zelong Li, Shuyuan Xu +4
In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under th…
Counterfactual Collaborative Reasoning
Jianchao Ji, Zelong Li, Shuyuan Xu +5
Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under ma…
UP5: Unbiased Foundation Model for Fairness-aware Recommendation
Wenyue Hua, Yingqiang Ge, Shuyuan Xu +2
Recent advances in Foundation Models such as Large Language Models (LLMs) have propelled them to the forefront of Recommender Systems (RS). Despite their utility, there is a growin…
Causal Inference for Recommendation: Foundations, Methods and Applications
Shuyuan Xu, Jianchao Ji, Yunqi Li +3
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recomme…
Dynamic Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Zuohui Fu +3
Causal 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 loo…
A Survey on Trustworthy Recommender Systems
Yingqiang Ge, Shuchang Liu, Zuohui Fu +6
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. How…
Fairness in Recommendation: Foundations, Methods and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu +4
As 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 t…
AutoLossGen: Automatic Loss Function Generation for Recommender Systems
Zelong Li, Jianchao Ji, Yingqiang Ge +1
In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a b…
Explainable Fairness in Recommendation
Yingqiang Ge, Juntao Tan, Yan Zhu +7
Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies…