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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/54093

Title: Towards Explainable Personalized Recommendations by Learning from Users’ Photos
Author(s): Díez Peláez, Jorge
Pérez Núñez, Pablo
Luaces Rodríguez, Óscar
Remeseiro López, Beatriz
Bahamonde Rionda, Antonio
Keywords: Artificial Intelligence
Issue date: 2020
Publisher: Elsevier
Publisher version: http://dx.doi.org/10.1016/j.ins.2020.02.018
Format extent: p. 416-430
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.
Embargo date: 2022-02-10
URI: http://hdl.handle.net/10651/54093
Sponsored: This work was funded under grants TIN2015-65069-C2-2-R from the Spanish Ministry of the Economy and Competitiveness, and IDI-2018-000176 from the Principado de Asturias Regional Government, partially supported with ERDF funds. We are grateful to NVIDIA Corporation for the donation of the Titan Xp GPU used in this research.
Project id.: TIN2015-65069-C2-2-R
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