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 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 ?
To overcome this problem, we propose a novel e-learning recommender system that recommends interesting information to the learners, thus save learners' time and improve their learning performance.
Also, participants were found to be quite active across several anti-vaccination pages, "suggesting that users' activity on anti-vaccination is more than just a product of Facebook's recommender system (a system that recommends like-minded groups to people)," Smith added.
ISLAMABAD -- Ministry of Information Technology and Telecommunications (MoIT) would introduce a state-of-the-art high performance recommender system to help avoid spam that comes with marketing avenues of all kinds including SMS and emails.
This research will result into a recommender system that allows the retail investor to save a lot of time in locating potentially profitable trading opportunities.
In this paper, a new framework for including neutrosophic in knowledge based recommender system is presented.
Keywords: Recommender System, Food, eHealth, ACO, Cloud Computing, Pathological Reports
20] describes a personalized recommender system to shoppers in supermarkets rely on their previous behavior towards the purchases to suggest new products for them.
One of the successful model based CF in recommendation system has been matrix factorization (also, known as SVD) which addresses the problem of recommender system, also referred as state-of-art in recommender system (Cremonesi, Koren, & Turrin, 2010; Jawaheer, Weller, & Kostkova, 2014; Konstan & Riedl, 2012).
It occurs when the recommender system is short of ratings.
One of the tools that address this challenge is the recommender system, which is attracting a lot of attention recently [1-4].
As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find the best recommender system among a given set of alternatives.