214 citations · 1.2k across the 32 of their papers we have counts for
24 papers · 1 filter
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems
Shuyuan Xu, Wenyue Hua, Yongfeng Zhang
In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers. Despite this interest, the…
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
VIP5: Towards Multimodal Foundation Models for Recommendation
Shijie Geng, Juntao Tan, Shuchang Liu +2
Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resul…
How to Index Item IDs for Recommendation Foundation Models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge +1
Recommendation foundation model utilizes large language models (LLM) for recommendation by converting recommendation tasks into natural language tasks. It enables generative recomm…
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang +2
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…
Transferable Fairness for Cold-Start Recommendation
Yunqi Li, Dingxian Wang, Hanxiong Chen +1
With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem. Previous works on fairness-…
Causal Structure Learning with Recommendation System
Shuyuan Xu, Da Xu, Evren Korpeoglu +4
A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by us…
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Ziheng Chen, Fabrizio Silvestri, Jia Wang +4
Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system t…
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
Shijie Geng, Shuchang Liu, Zuohui Fu +2
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned kno…
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation
Xu Chen, Yongfeng Zhang, Ji-Rong Wen
Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency, among others. A fundamental problem o…
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
Juntao Tan, Shijie Geng, Zuohui Fu +4
Structural 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 comp…
Personalized Prompt Learning for Explainable Recommendation
Lei Li, Yongfeng Zhang, Li Chen
Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' t…
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
Yingqiang Ge, Xiaoting Zhao, Lucia Yu +4
The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware r…
Graph Collaborative Reasoning
Hanxiong Chen, Yunqi Li, Shaoyun Shi +3
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. Ho…
Deconfounded Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Shelby Heinecke +2
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation pe…
Counterfactual Explainable Recommendation
Juntao Tan, Shuyuan Xu, Yingqiang Ge +3
By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem.…
Personalized Transformer for Explainable Recommendation
Lei Li, Yongfeng Zhang, Li Chen
Personalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In thes…
Personalized Counterfactual Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Shuyuan Xu +2
Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Faithfully Explainable Recommendation via Neural Logic Reasoning
Yaxin Zhu, Yikun Xian, Zuohui Fu +2
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation pr…
Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li +3
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlat…
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
Lei Li, Yongfeng Zhang, Li Chen
Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender syst…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…
Neural Collaborative Reasoning
Hanxiong Chen, Shaoyun Shi, Yunqi Li +1
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models,…