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Collaborative filtering information


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.
Recommender systems
Concepts
  • Collective intelligence
  • Relevance
  • Star ratings
  • Long tail
Methods and challenges
  • Cold start
  • Collaborative filtering
  • Dimensionality reduction
  • Implicit data collection
  • Item-item collaborative filtering
  • Matrix factorization
  • Preference elicitation
  • Similarity search
Implementations
  • Collaborative search engine
  • Content discovery platform
  • Decision support system
  • Music Genome Project
  • Product finder
Research
  • GroupLens Research
  • MovieLens
  • Netflix Prize
  • ACM Conference on Recommender Systems
  • v
  • t
  • e

Collaborative filtering (CF) is a technique 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 the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. For example, a collaborative filtering recommendation system for preferences in television programming could make predictions about which television show a user should like given a partial list of that 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 for 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 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.

  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

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Collaborative filtering

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one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting...

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collaborators, and financial services. Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also...

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systems typically use collaborative filtering approaches or a combination of the collaborative filtering and content-based filtering approaches, although...

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Slope One is a family of algorithms used for collaborative filtering, introduced in a 2005 paper by Daniel Lemire and Anna Maclachlan. Arguably, it is...

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using one or more of the following: Collaborative filtering Semantic analysis Social rating Collaborative filtering is a method of forecasting often used...

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Robust collaborative filtering

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Robust collaborative filtering, or attack-resistant collaborative filtering, refers to algorithms or techniques that aim to make collaborative filtering more...

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MovieLens

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for its users to watch, based on their film preferences using collaborative filtering of members' movie ratings and movie reviews. It contains about...

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Matrix completion

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matrix completion are summarized by Candès and Plan as follows: Collaborative filtering is the task of making automatic predictions about the interests...

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Netflix Prize

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The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings...

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Dimensionality reduction

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variables (also called features or attributes). The three strategies are: the filter strategy (e.g. information gain), the wrapper strategy (e.g. search guided...

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Collaborative search engine

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and sharing of knowledge. Implicit collaboration characterizes Collaborative filtering and recommendation systems in which the system infers similar information...

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Decision support system

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challenges Cold start Collaborative filtering Dimensionality reduction Implicit data collection Item-item collaborative filtering Matrix factorization...

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Relevance

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challenges Cold start Collaborative filtering Dimensionality reduction Implicit data collection Item-item collaborative filtering Matrix factorization...

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Collective intelligence

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learning algorithms. Citizen science Civic intelligence Collaborative filtering Collaborative innovation network Collective decision-making Collective...

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Long tail

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comes to expanding the long tail. Some recommenders (i.e. certain collaborative filters) can exhibit a bias toward popular products, creating positive feedback...

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Loren Terveen

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Minneapolis-Saint Paul, Minnesota. Terveen co-authored the article "Evaluating collaborative filtering recommender systems", which has been cited almost four thousand...

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between reputation systems and collaborative filtering is the ways in which they use user feedback. In collaborative filtering, the goal is to find similarities...

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machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification. Apache Mesos Apache Mesos abstracts...

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his vision for an information economy, they began working on a collaborative filtering system for Usenet news. The system collected ratings from Usenet...

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user behavior and activity. A variety of algorithms can be used: Collaborative filtering of different users' behavior, preferences, and ratings. Automatic...

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sales and overall spending of an individual. A process known as "collaborative filtering" tries to analyse common products of interest for an individual...

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Similarity search

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challenges Cold start Collaborative filtering Dimensionality reduction Implicit data collection Item-item collaborative filtering Matrix factorization...

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