6 papers · 1 filter
A Survey on Generative Recommendation: Data, Model, and Tasks
Min Hou, Le Wu, Yuxin Liao +6
Recommender systems serve as foundational infrastructure in modern information ecosystems, helping users navigate digital content and discover items aligned with their preferences.…
MLLMRec-R1: Incentivizing Reasoning Capability in Large Language Models for Multimodal Sequential Recommendation
Yu Wang, Yonghui Yang, Le Wu +3
Group relative policy optimization (GRPO) has become a standard post-training paradigm for improving reasoning and preference alignment in large language models (LLMs), and has rec…
From Atom to Community: Structured and Evolving Agent Memory for User Behavior Modeling
Yuxin Liao, Le Wu, Min Hou +3
User behavior modeling lies at the heart of personalized applications like recommender systems. With LLM-based agents, user preference representation has evolved from latent embedd…
An Efficient LLM-based Evolutional Recommendation with Locate-Forget-Update Paradigm
Hao Liu, Le Wu, Min Hou +4
Nowadays, Large Language Models (LLMs) have shown exceptional performance in sequential recommendations, and the adoption of LLM-based recommender systems (LLMRec) is becoming incr…
WeaveRec: An LLM-Based Cross-Domain Sequential Recommendation Framework with Model Merging
Min Hou, Xin Liu, Le Wu +5
Cross-Domain Sequential Recommendation (CDSR) seeks to improve user preference modeling by transferring knowledge from multiple domains. Despite the progress made in CDSR, most exi…
RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation
Min Hou, Chenxi Bai, Le Wu +6
Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the…