2 citations · 2 across the 2 of their papers we have counts for
7 papers
Dual Side Deep Context-aware Modulation for Social Recommendation
Bairan Fu, Wenming Zhang, Guangneng Hu +3
Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users…
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
Guangneng Hu, Qiang Yang
We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often f…
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
Guangneng Hu, Qiang Yang
Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performa…
Personalized Neural Embeddings for Collaborative Filtering with Text
Guangneng Hu
Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they…
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Guangneng Hu, Yu Zhang, Qiang Yang
Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data spa…
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
Guangneng Hu, Yu Zhang, Qiang Yang
The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfe…