Selective Forgetting for Incremental Matrix Factorization in Recommender Systems

Recommender Systems are used to build models of users' preferences. Those models should reflect current state of the preferences at any timepoint. The preferences, however, are not static. They are subject to concept drift or even shift, as it is known from e.g. stream mining. They undergo permanent changes as the taste of users and perception of items change over time. Therefore, it is crucial to select the actual data for training models and to forget the outdated ones. The problem of selective forgetting in recommender systems has not been addressed so far. Therefore, we propose two forgetting techniques for incremental matrix factorization and incorporate them into a stream recommender. We use a stream-based algorithm that adapts continuously to changes, so that forgetting techniques have an immediate effect on recommendations. We introduce a new evaluation protocol for recommender systems in a streaming environment and show that forgetting of outdated data increases the quality of recommendations substantially.