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20242026
most citedLarge Language Models and Causal Inference in Collaboration: A Survey

14 citations · 32 across the 38 of their papers we have counts for

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6 papers · 1 filter

cs.IR2026

Evaluation on Entity Matching in Recommender Systems

Zihan Huang, Rohan Surana, Zhouhang Xie +3

Entity matching is a crucial component in various recommender systems, including conversational recommender systems (CRS) and knowledge-based recommender systems. However, the lack…

cs.IR2025

The 2nd Workshop on Human-Centered Recommender Systems

Kaike Zhang, Jiakai Tang, Du Su +6

Recommender systems shape how people discover information, form opinions, and connect with society. Yet, as their influence grows, traditional metrics, e.g., accuracy, clicks, and…

cs.IR2025

Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction

Hongtao Huang, Chengkai Huang, Junda Wu +3

Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…

cs.IR2025

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms

Chengkai Huang, Hongtao Huang, Tong Yu +6

Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…

cs.IR2025

Toward Holistic Evaluation of Recommender Systems Powered by Generative Models

Yashar Deldjoo, Nikhil Mehta, Maheswaran Sathiamoorthy +3

Recommender systems powered by generative models (Gen-RecSys) extend beyond classical item ranking by producing open-ended content, which simultaneously unlocks richer user experie…

cs.IR2024★ 3 cited

Foundation Models for Recommender Systems: A Survey and New Perspectives

Chengkai Huang, Tong Yu, Kaige Xie +3

Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).…