29 citations · 29 across the 9 of their papers we have counts for
10 papers
LeanMem: Simple and Efficient Long-Term Memory for LLM Agents
Yuxin Liao, Le Wu, Min Hou +3
Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history. However, existing memory systems typically process heterogeneous d…
VC-Soup: Value-Consistency Guided Multi-Value Alignment for Large Language Models
Hefei Xu, Le Wu, Yu Wang +4
As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values has become a central objecti…
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…
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Miaomiao Cai, Min Hou, Lei Chen +4
Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based…
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…
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.…
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…
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models
Min Hou, Yueying Wu, Chang Xu +4
As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-…
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation
Haoyue Bai, Le Wu, Min Hou +5
Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge numb…