7 papers · 1 filter
PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
Kepu Zhang, Teng Shi, Weijie Yu +1
Personalized retrieval-augmented generation (RAG) aims to produce user-tailored responses by incorporating retrieved user profiles alongside the input query. Existing methods prima…
MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation
Weicong Qin, Yi Xu, Weijie Yu +5
Large language models (LLMs) have emerged as a cutting-edge approach in sequential recommendation, leveraging historical interactions to model dynamic user preferences. Current met…
Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu +6
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Levera…
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment
Weicong Qin, Yi Xu, Weijie Yu +5
Personalized product search aims to retrieve and rank items that match users' preferences and search intent. Despite their effectiveness, existing approaches typically assume that…
Decoding Recommendation Behaviors of In-Context Learning LLMs Through Gradient Descent
Yi Xu, Weicong Qin, Weijie Yu +3
Recently, there has been a growing trend in utilizing large language models (LLMs) for recommender systems, referred to as LLMRec. A notable approach within this trend is not to fi…
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Changshuo Zhang, Sirui Chen, Xiao Zhang +3
Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests. However, modern recommend…