62 citations · 145 across the 9 of their papers we have counts for
9 papers
Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv +6
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In…
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…
Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction
Hong Wen, Jing Zhang, Fuyu Lv +3
Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to a…
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)…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Yufei Feng, Binbin Hu, Fuyu Lv +3
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts m…
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…
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…
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…