84 citations · 124 across the 4 of their papers we have counts for
9 papers
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…
AliCoCo: Alibaba E-commerce Cognitive Concept Net
Xusheng Luo, Luxin Liu, Yonghua Yang +6
One of the ultimate goals of e-commerce platforms is to satisfy various shopping needs for their customers. Much efforts are devoted to creating taxonomies or ontologies in e-comme…
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…
Conceptualize and Infer User Needs in E-commerce
Xusheng Luo, Yonghua Yang, Kenny Q. Zhu +2
Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solution…
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu +4
Post-click conversion rate (CVR) estimation is a critical task in e-commerce recommender systems. This task is deemed quite challenging under the industrial setting with two major…
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction
Hong Wen, Jing Zhang, Yuan Wang +4
Recommender system, as an essential part of modern e-commerce, consists of two fundamental modules, namely Click-Through Rate (CTR) and Conversion Rate (CVR) prediction. While CVR…