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Sparsity Effect on Collaborative Filtering-based Personalized Recommendation  

Kim, Jong-Woo (한양대학교 경영학부)
Bae, Se-Jin (충남대학교 자연대학 통계학과)
Lee, Hong-Joo (한국과학기술원 테크노경영대학원)
Publication Information
Asia pacific journal of information systems / v.14, no.2, 2004 , pp. 131-149 More about this Journal
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
personalization; recommendation techniques; collaborative filtering; sparsity;
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