Personalized Recommendation for Individual Users Based on the Group Recommendation Principles

Michal KOMPAN, Mária BIELIKOVÁ

Abstract

The amount of information available on the Web is increasing day by day. Users are overloaded and cannot access desired information in a reasonable time. Plenty of approaches for the web personalization, which try to solve information overload have been proposed in the literature. Important feature of any personalization is its accuracy, or more precisely accuracy of personalized recommendation provided to a user. In this paper we propose a new recommendation approach for the single-user collaborative filtering based on the principles of the recommendations for group of users. We explore the best configuration for such an approach according to the group size used for computation, the aggregation strategy of ratings used within groups or the number of similar users used for the recommendation. We did an experiment over the MovieLens and SME.SK news portal datasets. Proposed approach is compared to the standard collaborative and group recommender respectively. The results support our hypothesis that the proposed approach brings statistically significant improvement and it is applicable on various domains, thus can be used for the single-user recommendation tasks.

Keywords

Collaborative filtering, group recommendation, virtual groups, aggregation strategies.

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