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http://dx.doi.org/10.3745/KTCCS.2013.2.11.475

Improved Movie Recommendation System based-on Personal Propensity and Collaborative Filtering  

Park, Doo-Soon (순천향대학교 컴퓨터소프트웨어공학과)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.2, no.11, 2013 , pp. 475-482 More about this Journal
Abstract
Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.
Keywords
Recommender Systems; Collavorative Filtering; Sparsity; Personal Propensity;
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Times Cited By KSCI : 1  (Citation Analysis)
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