Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Sparsity Effect on Collaborative Filtering-based Personalized Recommendation

협업 필터링 기반 개인화 추천에서의 평가자료의 희소 정도의 영향

  • 김종우 (한양대학교 경영학부) ;
  • 배세진 (충남대학교 자연대학 통계학과) ;
  • 이홍주 (한국과학기술원 테크노경영대학원)
  • Published : 2004.06.30
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Abstract

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

Keywords

References

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