Tag-Based User Profiling: A Game Theoretic Approach

Abstract

As already pointed out by a constantly growing literature, explainability in recommender systems field is a key aspect to increase users' satisfaction. With the increase of user generated content, tags have proven to be highly relevant when it comes to describe either users or items. A number of strategies that rely on tags have been proposed, yet, many of these algorithms exploit the frequency of user-tags interactions to gain information. We argue that a pure frequentist description might lack of specificity to grasp user's peculiar tastes. Therefore, we propose a novel approach based on game theory that tries to find the best trade-off between generality and detailing. The identified user's description can be used to keep her in the loop and allows the user to have control over system's knowledge. Additionally, we propose a user interface that embeds the proposed user's description and it can be used by the user herself to guide her catalogue's exploration toward novel and serendipitous items.