Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
A hybrid approach for movie recommendation
Multimedia Tools and Applications
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering Recommender Systems Using Tag Information
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Improved neighborhood-based algorithms for large-scale recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Modeling collaborative semantics with a geographic recommender
ER'07 Proceedings of the 2007 conference on Advances in conceptual modeling: foundations and applications
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Enhanced vector space models for content-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
A Framework for Recommender Systems in E-Commerce Based on Distributed Storage and Data-Mining
ICEE '10 Proceedings of the 2010 International Conference on E-Business and E-Government
Pagerank-based collaborative filtering recommendation
ICICA'10 Proceedings of the First international conference on Information computing and applications
Expert Systems with Applications: An International Journal
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In item-based collaborative filtering, a critical intermediate step to personalized recommendations is the definition of an item-similarity metric. Existing algorithms compute the item-similarity using the user-to-item ratings (cosine, Pearson, Jaccard, etc.). When computing the similarity between two items A and B many of these algorithms divide the actual number of co-occurring users by some "difficulty" of co-occurrence. We refine this approach by defining item similarity as the ratio of the actual number of co-occurrences to the number of co-occurrences that would be expected if user choices were random. In the final step of our method to compute personalized recommendations we apply the usage history of a user to the item similarity matrix. The well defined probabilistic meaning of our similarities allows us to further improve this final step. We measured the quality of our algorithm on a large real-world data-set. As part of Comcast's efforts to improve its personalized recommendations of movies and TV shows, several top recommender companies were invited to apply their algorithms to one year of Video-on-Demand usage data. Our algorithm tied for first place. This paper includes a MapReduce pseudo code implementation of our algorithm.