collaborative filtering


Also found in: Acronyms, Wikipedia.

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 website; 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 ?
Previous works have been conducted on similarity calculation among OLAP queries to recommend a single query or session using collaborative filtering techniques.
In each of those examples, the critical thing to take note of is that we are talking about how groups of people behave within the context of a particular task--this is the "collaborative" that lies at the heart of collaborative filtering.
Improving network topology -based protein interactome mapping via collaborative filtering," knowl-Based syst.
Good and his coauthors were honored for developing an effective way to combine collaborative filtering and content filtering to provide better recommendations to users.
Table I is the rating matrix of collaborative filtering recommendation, put U = {[u.
The collaborative filtering is based on the assumption that consensuses people in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.
Memory-based algorithms in Collaborative filtering (Breese et al.
Recently, collaborative filtering algorithm has been widely studied in both academic and industrial fields.
2009) suggested that learners with greater knowledge should have greater weight in the computation of recommendation than the learners with less knowledge among all neighbors of an active learner in collaborative filtering framework.
There are different types of recommender systems like collaborative filtering recommender systems, content-based filtering recommender systems, hybrid filtering recommender systems, knowledge-based filtering recommender systems, demographic-based filtering recommender systems and utility-based filtering recommender systems [6, 8-9, 23].
As far as we known, study in [4] is the first work of predicting Web service's QoS through collaborative filtering (CF).

Full browser ?