17 citations · 24 across the 5 of their papers we have counts for
5 papers
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…
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System
Fuyu Lv, Mengxue Li, Tonglei Guo +4
Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based Retrieval (EBR)…
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
Fuyu Lv, Taiwei Jin, Changlong Yu +4
Capturing users' precise preferences is a fundamental problem in large-scale recommender system. Currently, item-based Collaborative Filtering (CF) methods are common matching appr…
Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
Hong Wen, Jing Zhang, Quan Lin +2
Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named "Click-Through Rate P…