Towards Explainable Personalized Recommendations by Learning from Users' Photos

Explaining the output of a complex system, such as a Recommender System (RS), is becoming of utmost importance for both users and companies. In this paper we explore the idea that personalized explanations can be learned as recommendation themselves. There are plenty of online services where users can upload some photos, in addition to rating items. We assume that users take these photos to reinforce or justify their opinions about the items. For this reason we try to predict what photo a user would take of an item, because that image is the argument that can best convince her of the qualities of the item. In this sense, an RS can explain its results and, therefore, increase its reliability. Furthermore, once we have a model to predict attractive images for users, we can estimate their distribution. The paper includes a formal framework that estimates the authorship probability for a given pair (user, photo). To illustrate the proposal, we use data gathered from TripAdvisor containing the reviews (with photos) of restaurants in six cities of different sizes. Keywords: Recommender Systems, Personalization, Explainability, Photo, Collaborative 1. Introduction Explainable Artificial Intelligence (XAI) is becoming an important area of interest since explainability is increasingly necessary to meet stakeholder demands. In particular, the General Data Protection Regulation (GDPR) [29] of the European Union demands transparency in systems that take decisions affecting people, making explanations more needed than ever. Additionally, explanations may help increase the trust of users in AI algorithms, since people rely not only on their efficacy but also on the degree of understanding of the process they follow. Since they provide suggestions to users, explainability plays an important role on them.