91 citations · 183 across the 22 of their papers we have counts for
11 papers · 1 filter
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Yuyuan Li, Chaochao Chen, Xiaolin Zheng +3
Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Be…
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han +3
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…
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…
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
Yuyuan Li, Xiaolin Zheng, Chaochao Chen +1
Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the…
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…
Multi-Facet Recommender Networks with Spherical Optimization
Yanchao Tan, Carl Yang, Xiangyu Wei +2
Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interact…
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
Menghan Wang, Xiaolin Zheng, Kun Zhang
This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized d…
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
Menghan Wang, Xiaolin Zheng, Yang Yang +1
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar prefer…