Filtragem colaborativa

Filtragem colaborativa (FC) é uma técnica utilizada por alguns sistemas de recomendação.

Ver também

• Sistemas de filtragem de informação
• Filtragem baseada em conteúdo
• Sistemas de recomendação
• Recuperação de informação

Referências

• Este artigo foi inicialmente traduzido, total ou parcialmente, do artigo da Wikipédia em japonês cujo título é «協調フィルタリング».

Ligações externas

• Beyond Recommender Systems: Helping People Help Each Other, page 12, 2001
• Recommender Systems. Prem Melville and Vikas Sindhwani. In Encyclopedia of Machine Learning, Claude Sammut and Geoffrey Webb (Eds), Springer, 2010.
• Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Adomavicius, G. and Tuzhilin, A. IEEE Transactions on Knowledge and Data Engineering 06.2005
• Evaluating collaborative filtering recommender systems (DOI: 10.1145/963770.963772)
• GroupLens research papers.
• Content-Boosted Collaborative Filtering for Improved Recommendations. Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan. Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187–192, Edmonton, Canada, July 2002.
• A collection of past and present "information filtering" projects (including collaborative filtering) at MIT Media Lab
• Eigentaste: A Constant Time Collaborative Filtering Algorithm. Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Information Retrieval, 4(2), 133-151. July 2001.
• Methods and Metrics for Cold-Start Recommendations
• A Survey of Collaborative Filtering Techniques Su, Xiaoyuan and Khoshgortaar, Taghi. M
• Google News Personalization: Scalable Online Collaborative Filtering Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. International World Wide Web Conference, Proceedings of the 16th international conference on World Wide Web
• Factor in the Neighbors: Scalable and Accurate Collaborative Filtering Yehuda Koren, Transactions on Knowledge Discovery from Data (TKDD) (2009)
• Rating Prediction Using Collaborative Filtering
• Recommender Systems
• Berkeley Collaborative Filtering