554 citations · 1.6k across the 46 of their papers we have counts for
6 papers · 2 filters
Multi-Relational Contrastive Learning for Recommendation
Wei Wei, Lianghao Xia, Chao Huang
Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platform…
HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation
Chenglei Shen, Xiao Zhang, Wei Wei +1
In real-world streaming recommender systems, user preferences often dynamically change over time (e.g., a user may have different preferences during weekdays and weekends). Existin…
SSLRec: A Self-Supervised Learning Framework for Recommendation
Xubin Ren, Lianghao Xia, Yuhao Yang +4
Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despit…
Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation
Sen Zhao, Wei Wei, Xian-Ling Mao +5
Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences…
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Sen Zhao, Wei Wei, Yifan Liu +6
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation. In each turn of C…
Multi-Modal Self-Supervised Learning for Recommendation
Wei Wei, Chao Huang, Lianghao Xia +1
The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and…