Does a one-size recommendation system fit all? the effectiveness of collaborative filtering based recommendation systems across different domains and search modes

Abstract

Collaborative filtering (CF) is a personalization technology that generates recommendations for users based on others' evaluations. CF is used by numerous e-commerce Web sites for providing personalized recommendations. Although much research has focused on refining collaborative filtering algorithms, little is known about the effects of user and domain characteristics on the accuracy of collaborative filtering systems. In this study, the effects of two factors—product domain and users' search mode—on the accuracy of CF are investigated. The effects of those factors are tested using data collected from two experiments in two different product domains, and from two large CF datasets, EachMovie and Book-Crossing. The study shows that the search mode of the users strongly influences the accuracy of the recommendations. CF works better when users look for specific information than when they search for general information. The accuracy drops significantly when data from different modes are mixed. The study also shows that CF is more accurate for knowledge domains than for consumer product domains. The results of this study imply that for more accurate recommendations, collaborative filtering systems should be able to identify and handle users' mode of search, even within the same domain and user group.