373 citations · 441 across the 9 of their papers we have counts for
9 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…
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
Guang-Neng Hu, Xin-Yu Dai, Feng-Yu Qiu +4
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collabo…
Integrating Reviews into Personalized Ranking for Cold Start Recommendation
Guang-Neng Hu, Xin-Yu Dai
Item recommendation task predicts a personalized ranking over a set of items for each individual user. One paradigm is the rating-based methods that concentrate on explicit feedbac…
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Guang-Neng Hu, Xin-Yu Dai, Yunya Song +2
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated cont…