Towards Explainable Personalized Recommendations by Learning from Users' Photos

TL;DR

This paper proposes a novel approach to explain recommendations by predicting user-uploaded photos that justify their opinions, enhancing transparency and providing insights into user preferences.

Contribution

It introduces a formal framework to predict user photos as explanations for recommendations, leveraging user-generated images to improve trust and understanding in recommender systems.

Findings

Model successfully predicts user photos for restaurant reviews.

Provides insights into user preferences through photo distribution analysis.

Enhances recommender system transparency and reliability.

Abstract

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. Thus, the companies acquire a vivid knowledge about the aspects that the clients highlight of their products. 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.