3 citations · 4 across the 8 of their papers we have counts for
8 papers
A Deep Behavior Path Matching Network for Click-Through Rate Prediction
Jian Dong, Yisong Yu, Yapeng Zhang +6
User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understandi…
A New Approach to Training Multiple Cooperative Agents for Autonomous Driving
Ruiyang Yang, Siheng Li, Beihong Jin
Training multiple agents to perform safe and cooperative control in the complex scenarios of autonomous driving has been a challenge. For a small fleet of cars moving together, thi…
Improving Micro-video Recommendation by Controlling Position Bias
Yisong Yu, Beihong Jin, Jiageng Song +3
As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-vi…
Improving Micro-video Recommendation via Contrastive Multiple Interests
Beibei Li, Beihong Jin, Jiageng Song +3
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more a…
HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
Pengfei Gao, Jingpeng Zhao, Yinglong Ma +2
Many important classification problems in the real-world consist of a large number of closely related categories in a hierarchical structure or taxonomy. Hierarchical multi-label t…
A Behavior-aware Graph Convolution Network Model for Video Recommendation
Wei Zhuo, Kunchi Liu, Taofeng Xue +7
Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which…
Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval
Hongyin Tang, Xingwu Sun, Beihong Jin +3
Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than tradit…
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Xinzhou Dong, Beihong Jin, Wei Zhuo +2
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of ite…