Fab: content-based, collaborative recommendation
Communications of the ACM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Knowledge and Information Systems
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Modern Information Retrieval
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection
Proceedings of the 7th ACM conference on Recommender systems
Sage: recommender engine as a cloud service
Proceedings of the 7th ACM conference on Recommender systems
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We study the problem of scoring and selecting content-based features for a collaborative filtering (CF) recommender system. Content-based features play a central role in mitigating the ``cold start'' problem in commercial recommenders. They are also useful in other related tasks, such as recommendation explanation and visualization. However, traditional feature selection methods do not generalize well to recommender systems. As a result, commercial systems typically use manually crafted and selected features. This work presents a framework for automated selection of informative content-based features, that is independent of the type of recommender system or the type of features. We evaluate on recommenders from different domains: books, movies and smart-phone apps, and show effective results on each. In addition, we show how to use the proposed methods to generate meaningful features from text.