13 papers · 1 filter
Token-Level Credit Assignment Optimization for Generative Document Retrieval
Xinpeng Zhao, Yang Liu, Ran Chen +6
Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, i.e…
Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators
Jiyuan Yang, Gengxin Sun, Mengqi Zhang +5
Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposur…
Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study
Wenhao Zhang, Ruihao Yu, Yi Bai +2
While generative retrieval (GR) demonstrates competitive performance on standard retrieval benchmarks, existing approaches directly map queries to document identifiers (docids) wit…
Model Editing for New Document Integration in Generative Information Retrieval
Zhen Zhang, Zihan Wang, Xinyu Ma +6
Generative retrieval (GR) reformulates the Information Retrieval (IR) task as the generation of document identifiers (docIDs). Despite its promise, existing GR models exhibit poor…
Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation
Lei Guo, Hongyun Yang, Pengjie Ren +3
Large Language Models have shown great success in recommender systems. However, the limited and sparse nature of user data often restricts the LLM's ability to effectively model be…
Curriculum Approximate Unlearning for Session-based Recommendation
Liu Yang, Zhaochun Ren, Ziqi Zhao +7
Approximate unlearning for session-based recommendation refers to eliminating the influence of specific training samples from the recommender without retraining of (sub-)models. Gr…