On Reducing User Interaction Data for Personalization

Abstract

Most recommender systems rely on user interaction data for personalization. Usually, the recommendation quality improves with more data. In this work, we study the quality implications when limiting user interaction data for personalization purposes. We formalize this problem and provide algorithms for selecting a smaller subset of user interaction data. We propose a selection method that picks the subset of a user’s history items that maximizes the expected recommendation quality. We show on well studied benchmarks that it is possible to achieve high quality results with small subsets of less than ten items per user.