113 citations · 158 across the 6 of their papers we have counts for
5 papers · 1 filter
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Fan Liu, Zhiyong Cheng, Huilin Chen +3
Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when c…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
As important side information, attributes have been widely exploited in the existing recommender system for better performance. In the real-world scenarios, it is common that some…
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Fan Liu, Zhiyong Cheng, Changchang Sun +3
Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. How…