collaborative filtering


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collaborative filtering

Also known as "social filtering" and "social information filtering," it refers to techniques that identify information people might be interested in. Collaborative filtering is used to create "recommendation systems" that can enhance the experience on a Web site; for example, by suggesting music or movies.

Different algorithms are used, but the basic principle is to develop a rating system for matching incoming material. "Collaborative" means that a group of people interested in the subject define their preferences when setting up the system. See collaborative software and music recommendation service.
References in periodicals archive ?
A collaborative filtering recommendation algorithm based on user clustering and items clustering.
He says, it's the same problem the dating industry solved with online dating websites using collaborative filtering technology.
As far as we known, study in [4] is the first work of predicting Web service's QoS through collaborative filtering (CF).
The recommendation module consists of two sub-modules: the data clustering module and the focused collaborative filtering module.
In general, collaborative filtering systems approach the problem of information filtering by estimating the desirability of items under consideration.
DataSage believes that the market is moving beyond mere collaborative filtering - the practice of predicting what users will want based on what other users have bought - to individualization, eventually creating a virtual store for each customer that visits a web site.
Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
In Item Based Collaborative Filtering 'k' items which are most similar to the current item are recommended to the user (Khabbaz and V S Lakshmanan, 2011).
The first paper deals with the Collaborative filtering wherein the authors have proposed collaborative filtering algorithm for recommendation systems.
The secret to Gvidi's great recommendations is its sophisticated collaborative filtering algorithms.
The main types of recommender systems namely collaborative filtering and content-based filtering surfer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage.

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