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


KIPS Transactions on Computer and Communication Systems, Vol. 2, No. 11, pp. 475-482, Nov. 2013
10.3745/KTCCS.2013.2.11.475,   PDF Download:

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.


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Cite this article
[IEEE Style]
D. S. Park, H. S. Eun, H. K. Oh, S. J. Kim, "Improved Movie Recommendation System based-on Personal Propensity and Collaborative Filtering," KIPS Transactions on Computer and Communication Systems, vol. 2, no. 11, pp. 475-482, 2013. DOI: 10.3745/KTCCS.2013.2.11.475.

[ACM Style]
Doo Soon Park, Ha Soo Eun, Hee Kuck Oh, and Sang Jin Kim. 2013. Improved Movie Recommendation System based-on Personal Propensity and Collaborative Filtering. KIPS Transactions on Computer and Communication Systems, 2, 11, (2013), 475-482. DOI: 10.3745/KTCCS.2013.2.11.475.