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- Created 2012-02-18
) is a technique used by some
s. Collaborative filtering has two senses, a narrow one and a more general one. In general, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.
In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or
information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person
has the same opinion as a person
on an issue, A is more likely to have B's opinion on a different issue
than to have the opinion on x of a person chosen randomly. For example, a collaborative filtering recommendation system for
tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an
(non-specific) score for each item of interest, for example based on its number of
from Wikipedia (last updated: 04 December), licensed under
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''Beyond Recommender Systems: Helping People Help Each Other''
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
Evaluating collaborative filtering recommender systems
GroupLens research papers
Content-Boosted Collaborative Filtering for Improved Recommendations.
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
Google News Personalization: Scalable Online Collaborative Filtering
Factor in the Neighbors: Scalable and Accurate Collaborative Filtering
Rating Prediction Using Collaborative Filtering
The Long Tail
Relevance (information retrieval)
Collaborative search engine
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