169 citations · 437 across the 13 of their papers we have counts for
6 papers · 1 filter
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou, Xianshuai Cao, Wenhao Li +4
Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out…
A Review-aware Graph Contrastive Learning Framework for Recommendation
Jie Shuai, Kun Zhang, Le Wu +4
Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the au…
Privileged Graph Distillation for Cold Start Recommendation
Shuai Wang, Kun Zhang, Le Wu +3
The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction…
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Shukui Ren +5
Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order…
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
Menghan Wang, Xiaolin Zheng, Kun Zhang
This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized d…
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
Menghan Wang, Xiaolin Zheng, Yang Yang +1
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar prefer…