364 citations · 460 across the 28 of their papers we have counts for
7 papers · 1 filter
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
Chunjing Gan, Bo Huang, Binbin Hu +6
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery,…
Long-tail Augmented Graph Contrastive Learning for Recommendation
Qian Zhao, Zhengwei Wu, Zhiqiang Zhang +1
Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usu…
Generative Contrastive Graph Learning for Recommendation
Yonghui Yang, Zhengwei Wu, Le Wu +5
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers hav…
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Sicong Xie, Binbin Hu, Fengze Li +4
Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which gr…
Improving Recommendation Fairness via Data Augmentation
Lei Chen, Le Wu, Kun Zhang +5
Collaborative filtering based recommendation learns users' preferences from all users' historical behavior data, and has been popular to facilitate decision making. R Recently, the…
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Kai Zhang, Hao Qian, Qi Liu +4
Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively stu…
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
Kai Zhang, Hao Qian, Qing Cui +5
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being ext…