24 citations · 44 across the 25 of their papers we have counts for
10 papers · 1 filter
Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation
Haiying He, Xiaopeng Li, Yuchen Gu +9
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user beh…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval
Yingyi Zhang, Junyi Li, Wenlin Zhang +8
Personalized large language models (LLMs) rely on memory retrieval to incorporate user-specific histories, preferences, and contexts. Existing approaches either overload the LLM by…
To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention
Wenlin Zhang, Kuicai Dong, Junyi Li +9
Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current agents…
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
Chao Zhang, Yuhao Wang, Derong Xu +9
Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability. For flexibility, agentic RAG employs autonomous, multi-round…
Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
Yingyi Zhang, Pengyue Jia, Derong Xu +9
Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies…