3 citations · 5 across the 5 of their papers we have counts for
5 papers
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation
Xiaolin Zheng, Jiajie Su, Weiming Liu +1
Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the perfo…
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Mengling Hu +1
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR…
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
Fan Wang, Weiming Liu, Chaochao Chen +2
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-…
Partial Relaxed Optimal Transport for Denoised Recommendation
Yanchao Tan, Carl Yang Member, Xiangyu Wei +2
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper…
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
Weiming Liu, Xiaolin Zheng, Mengling Hu +1
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the data sparsity and cold-start problem in recommender systems. In this…