정보처리학회논문지:컴퓨터 및 통신 시스템 (KIPS Transactions on Computer and Communication Systems)

  • 제2권11호
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  • Pages.475-482
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  • 2013
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  • 2287-5891(pISSN)
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  • 2734-049X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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개인성향과 협업 필터링을 이용한 개선된 영화 추천 시스템

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

  • 박두순 (순천향대학교 컴퓨터소프트웨어공학과)
  • 투고 : 2013.09.24
  • 심사 : 2013.10.28
  • 발행 : 2013.11.30
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초록

추천 시스템들에 대한 여러 방법들이 연구되고 있다. 개인화와 추천 시스템 중에서 가장 성공적인 방법은 협업 필터링이다. 협업 필터링은 고객들의 프로파일 정보를 기반으로 추천을 하므로 데이터가 충분하지 않다면 항목을 추천하는데 있어서 희박성의 문제가 제기된다. 본 연구에서는 희박성의 문제를 해결하는 방법으로 가중치를 가진 개인 성향을 협업 필터링에 활용하는 방법을 제안한다. 본 연구에서 가중치를 가진 최적의 개인 성향을 찾기 위해 공개 데이터인 MovieLens Data를 이용하여 성능 평가하였다. 실험 결과 본 연구에서 제안한 가중치를 가진 개인 성향들로 구축된 시스템이 기존의 개인 성향들을 이용한 시스템보다 향상된 성능을 보였다.

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.

키워드

참고문헌

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피인용 문헌

  1. Case Study of Big Data-Based Agri-food Recommendation System According to Types of Customers vol.40, pp.5, 2015, https://doi.org/10.7840/kics.2015.40.5.903
  2. Hybrid Preference Prediction Technique Using Weighting based Data Reliability for Collaborative Filtering Recommendation System vol.19, pp.5, 2014, https://doi.org/10.9708/jksci.2014.19.5.061