Term Association Measures for Memory-based Recommender Systems

The adaptation of Information Retrieval techniques for the item recommendation task has become a fertile research area. Previous works have established the correspondence between these two fields that allowed to adapt several retrieval techniques successfully. One line of study aims to model the item recommendation problem as a profile expansion task following the methods for query expansion in pseudo-relevance feedback. To solve the query expansion task in ad-hoc retrieval, several term association measures have been proposed in the past. In this paper, we adapt several of these measures to the top-N recommendation problem, specifically to the collaborative filtering scenario. Moreover, we perform experiments to study their effectiveness regarding accuracy, diversity and novelty. Our results show that some of the proposed measures can improve these aspects over well-known and commonly used recommendation similarity metrics (cosine similarity and Pearson's correlation coefficient).