84 citations · 123 across the 4 of their papers we have counts for
4 papers
Explainable Recommender with Geometric Information Bottleneck
Hanqi Yan, Lin Gui, Menghan Wang +2
Explainable recommender systems can explain their recommendation decisions, enhancing user trust in the systems. Most explainable recommender systems either rely on human-annotated…
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems
Menghan Wang, Yujie Lin, Guli Lin +2
Combining graph representation learning with multi-view data (side information) for recommendation is a trend in industry. Most existing methods can be categorized as \emph{multi-v…
Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Menghan Wang, Kun Zhang, Gulin Li +2
Graph Convolutional Networks (GCNs) have gained significant developments in representation learning on graphs. However, current GCNs suffer from two common challenges: 1) GCNs are…
Deep Session Interest Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Weichen Shen +4
Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and e…