Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Investigation of various matrix factorization methods for large recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Proceedings of the fourth ACM conference on Recommender systems
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Response prediction using collaborative filtering with hierarchies and side-information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Matrix co-factorization for recommendation with rich side information and implicit feedback
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Semantically enhanced collaborative filtering based on RSVD
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Hybrid recommendation based on low-dimensional augmentation of combined feature profiles
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
Enhancing matrix factorization through initialization for implicit feedback databases
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Improving recommendation accuracy based on item-specific tag preferences
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Like-Minded communities: bringing the familiarity and similarity together
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
A hidden Markov model for collaborative filtering
MIS Quarterly
Temporal influence over the Last.fm social network
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Cloud based real-time collaborative filtering for item-item recommendations
Computers in Industry
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The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as possible to the feature vectors generated by MF for CF. We propose methods for explicit feedback that are able to handle 140,000 features when feature vectors are very sparse. With movie metadata collected for the NP movies we show that the prediction performance of the methods is comparable to that of CF, and can be used to predict user preferences on new movies. We also investigate the value of movie metadata compared to movie ratings in regards of predictive power. We compare our solely CBF approach with a simple baseline rating-based predictor. We show that even 10 ratings of a new movie are more valuable than its metadata for predicting user ratings.