110 citations · 230 across the 9 of their papers we have counts for
9 papers
Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +6
Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a r…
CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process
Xiaodong Li, Jiawei Sheng, Jiangxia Cao +3
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by tra…
Contrastive Cross-Domain Sequential Recommendation
Jiangxia Cao, Xin Cong, Jiawei Sheng +2
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challen…
Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck
Shiyao Cui, Jiangxia Cao, Xin Cong +4
This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of…
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Gehang Zhang, Bowen Yu, Jiangxia Cao +4
Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different ``views'' of the same graph in order to learn node/gr…
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
Jiangxia Cao, Jiawei Sheng, Xin Cong +2
Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain R…