Relevance-based language modelling for recommender systems

• A new recommendation approach based on the Relevance Modelling (RM) of the problem is proposed.
• The neighbour selection problem is improved by Posterior Probabilistic Clustering (PPC).
• Background information such as item popularity is successfully integrated by using RM models.
• Performance of the recommendation improves when more clusters are considered in the PPC technique.
• Combination of both contributions leads to an even better performance than their separate application.

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.