Novelty and diversity enhancement and evaluation in Recommender Systems

Résumé

Recommender Systems have become a pervasive technology in a wide spectrum of everyday applications, and can be said to be familiar to the general public. In situations where there is an information overload, such as e-commerce, streaming platforms or social networks, providing personalized recommendations has proven to be a major source of enhanced functionality, user satisfaction, and revenue improvements. The development of recommendation algorithms and technologies has typically focused on maximizing the prediction accuracy of the user¿s interests. However, there is an increasing awareness in the field that there are other properties that have an impact on user satisfaction and business performance. In particular, there are many cases where applying some degree of novelty or diversity may be beneficial for both the users that receive the recommendations and the business that provides them. In this thesis we develop a principled approach to the evaluation and enhancement of novelty and diversity in Recommender Systems. We consider that the improvement of such fundamental dimensions of the usefulness of recommendations has to take into account how users explore and perceive recommendations, what are the problems that novelty and diversity solve and the causes of such problems. We propose in our first contribution a unified framework for the evaluation and enhancement of novelty and diversity in recommendations that generalizes and enhances many of the proposals previously studied in the state of the art under a common basis. Special emphasis is done in the study of the diversity within recommendations lists, for which two different contributions are presented. On the one hand, an adaptation of search result diversification metrics and techniques from Information Retrieval is explored to cope with the ambiguity of user interests and tastes. On the other hand, a domain-specific solution for assessing and optimizing the diversity of recommendations is proposed to address the need of users for varied recommendations when genre information about the recommendation domain is available. Finally, we address diversity as an overall quality from the system point of view, and we propose solutions for the problem in this perspective by turning the recommendation task around and recommending users to items. Our proposals are tested on a common experimental design that considers three different datasets for movie and music recommendation and four well-known baseline recommendation algorithms. The results of our experiments support the validity of our contributions and allow the analysis and further insights on their behavior when applied to different settings.