Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering
- Authors
- Jeong, Woon-hae; Kim, Se-jun; Park, Doo-soon; Kwak, Jin
- Issue Date
- Mar-2013
- Publisher
- 한국정보처리학회
- Keywords
- Collaborative Filtering; Movie Recommendation System; Personal Propensity; Security; Push Stack
- Citation
- JIPS(Journal of Information Processing Systems), v.9, no.1, pp 157 - 172
- Pages
- 16
- Journal Title
- JIPS(Journal of Information Processing Systems)
- Volume
- 9
- Number
- 1
- Start Page
- 157
- End Page
- 172
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/13880
- DOI
- 10.3745/JIPS.2013.9.1.157
- ISSN
- 1976-913X
2092-805X
- Abstract
- There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is 'collaborative filtering', which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time, we adopt 'push attack' principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system
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Collections - College of Engineering > Department of Information Security Engineering > 1. Journal Articles
- College of Engineering > Department of Computer Software Engineering > 1. Journal Articles
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