Collaborative filtering

This image shows an example of predicting of the user's rating using collaborative filtering. At first, people rate different items (like videos, images, games). After that, the system is making predictions about user's rating for an item, which the user has not rated yet. These predictions are built upon the existing ratings of other users, who have similar ratings with the active user. For instance, in our case the system has made a prediction, that the active user will not like the video.
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Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems.[1] Collaborative filtering has two senses, a narrow one and a more general one.[2]

In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about a user's interests by utilizing preferences or taste information collected from many users (collaborating). This approach assumes that if persons A and B share similar opinions on one issue, they are more likely to agree on other issues compared to a random pairing of A with another person. For instance, a collaborative filtering system for television programming could predict which shows a user might enjoy based on a limited list of the user's tastes (likes or dislikes).[3] These predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.

In the more general sense, collaborative filtering is the process of filtering information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc.[2] Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many 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; and user data from electronic commerce and web applications.

This article focuses on collaborative filtering for user data, but some of the methods also apply to other major applications.

  1. ^ Francesco Ricci and Lior Rokach and Bracha Shapira, Introduction to Recommender Systems Handbook Archived 2 June 2016 at the Wayback Machine, Recommender Systems Handbook, Springer, 2011, pp. 1–35
  2. ^ a b Terveen, Loren; Hill, Will (2001). "Beyond Recommender Systems: Helping People Help Each Other" (PDF). Addison-Wesley. p. 6. Retrieved 16 January 2012.
  3. ^ An integrated approach to TV & VOD Recommendations Archived 6 June 2012 at the Wayback Machine