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Number of Ratings and Performance in Collaborative Filtering-based Product Recommendation  

Lee Hong-Joo (한국과학기술원 테크노경영대학원)
Park Sung-Joo (한국과학기술원 테크노경영대학원)
Kim Jong-Woo (한양대학교 경영학부)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.31, no.2, 2006 , pp. 27-39 More about this Journal
Abstract
The Collaborative Filtering (CF) is one of the popular techniques for personalization in e-commerce storefronts. For CF-based recommendation, every customer needs to provide subjective evaluation ratings for some products based on his/her preference. Also, if an e-commerce site recommends a new product, some customers should rate it. However, there is no in-depth investigation on the impacts on recommendation performance of two number of ratings, i.e. the number of ratings of an individual customer and the number of ratings of an item, even though these are important factors to determine performance of CF methods. In this study, using publicly available EachMovie data set, we empirically investigate the relationships between the two number of ratings and the performance of CF. For the purpose, three analyses were executed. The first and second analyses were performed to investigate the relationship between the number of ratings of a particular customer and the recommendation performance of CF. In the third analysis, we investigate the relationship between the number of ratings on a particular item and the recommendation performance of CF. From these experiments, we can find that there are thresholds in terms of the number of ratings below which the recommendation performances increase monotonically. That is, the number of ratings of a customer and the number of ratings on an item are critical to the recommendation performance of CF when the number of ratings is less than the thresholds, but the value of the ratings decreases after the numbers of ratings pass the thresholds. The results of the experiments provide insight to making operational decisions concerning collaborative filtering in practice.
Keywords
Collaborative Filtering; Product Recommendation; Personalization; e-Commerce;
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