103 citations · 196 across the 17 of their papers we have counts for
5 papers · 1 filter
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
Chengkai Liu, Jianling Wang, James Caverlee
In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of…
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Chengkai Liu, Jianghao Lin, Hanzhou Liu +2
Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models…
Countering Mainstream Bias via End-to-End Adaptive Local Learning
Jinhao Pan, Ziwei Zhu, Jianling Wang +2
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for ma…
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Chengkai Liu, Jianghao Lin, Jianling Wang +2
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven t…
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
Jianling Wang, Haokai Lu, James Caverlee +2
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation…