80 citations · 481 across the 41 of their papers we have counts for
11 papers · 1 filter
Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation
Xin Xia, Junliang Yu, Guandong Xu +1
On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving u…
Counterfactual Explanation for Fairness in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +2
Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promot…
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +3
As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and inte…
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +1
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfact…
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Xin Xia, Hongzhi Yin, Junliang Yu +3
Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user…
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +3
Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' t…
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Yicong Li, Hongxu Chen, Yile Li +3
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing expl…
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
Haoran Yang, Hongxu Chen, Lin Li +2
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via le…
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Sixiao Zhang, Hongxu Chen, Xiao Ming +3
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space sho…
Hyperbolic Neural Collaborative Recommender
Anchen Li, Bo Yang, Hongxu Chen +1
This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic…
FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint
Yao Wu, Jian Cao, Guandong Xu
An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead t…