Understanding the Temporal Dynamics of Recommendations across Different Rating Scales

Libraries have large growing book collections and users have difficulty in browsing the whole collection when choosing new books to read, particularly when looking for books without a defined goal. In this case, recommendation systems are useful and play an important role in improving library usability. Recommendations are based on ratings and the quality of recommendations depends on the quality of the ratings. Studies show that users rate more items if scales have smaller granularity. In this paper, we propose a different rating scale for the book recommendation scenario in a collaborative filtering set-up and study how time influences rating relevance. Our findings suggest that the collaborative filtering algorithm benefits from a rating scale with smaller granularity. Moreover, if some conditions are met, rating prediction quality can be improved if we give lower weight to older ratings.