XPLODIV: An exploitation-exploration aware diversification approach for recommender systems

Recommender Systems (RS) have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional RS usually do not produce diverse results though it has been argued that diversity is a desirable feature. The study of diversity-aware RS has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval (IR). However, we argue it is not enough to adapt IR techniques to RS as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to RS. In this work, we propose a diversification technique for RS that generates a diversified list of results which not only balances the trade-off between quality (in terms of accuracy) and diversity, but also considers the trade-off between exploitation of the user profile and exploration of novel products. Our experimental evaluation shows that the proposed approach has comparable results to state of the art approaches. In addition, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations.