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Found 6 papers, 1 papers with code
Personalized Neural Embeddings for Collaborative Filtering with Text
We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations.
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods.
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.
Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback
First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i. e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics.
Integrating Reviews into Personalized Ranking for Cold Start Recommendation
On top of text features we uncover the review dimensions that explain the variation in users' feedback and these review factors represent a prior preference of users.
A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices.
