3 citations · 5 across the 3 of their papers we have counts for
5 papers
STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
Zhenye Yang, Jinpeng Chen, Huan Li +6
Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathe…
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
Jinpeng Chen, Jianxiang He, Huan Li +5
Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models o…
Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation
Fan Zhang, Jinpeng Chen, Huan Li +6
Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' info…
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation
Yuan Cao, Xudong Zhang, Fan Zhang +5
Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-bas…
SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network
Jinpeng Chen, Haiyang Li, Xudong Zhang +4
The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences accordi…