84 citations · 157 across the 7 of their papers we have counts for
9 papers · 1 filter
Modeling User Behavior with Graph Convolution for Personalized Product Search
Fan Lu, Qimai Li, Bo Liu +7
User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance b…
Embedding-based Product Retrieval in Taobao Search
Sen Li, Fuyu Lv, Taiwei Jin +5
Nowadays, the product search service of e-commerce platforms has become a vital shopping channel in people's life. The retrieval phase of products determines the search system's qu…
Learning a Product Relevance Model from Click-Through Data in E-Commerce
Shaowei Yao, Jiwei Tan, Xi Chen +4
The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential r…
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…
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…