Generating interpretable recommendations in recommender systems using context

Author(s)

Vitor Rodrigues Tonon

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2021-03-11

Examining board members

Solange Oliveira Rezende; Marcelo Garcia Manzato; Rafael Geraldeli Rossi; Diego Furtado Silva

• Advisor: Solange Oliveira Rezende

Abstract

Users face difficulties in choosing products and services on the Web because of the wide range of options. In this context, recommendation systems aim to assist users in identifying items of interest within a set of options. Traditional approaches to recommender systems focus on recommending more relevant items to individual users, not taking into account users context. However, in many real-world applications, it is also important to consider contextual information through the use of context-aware recommender systems. Several studies have indicated that using such information can improve the accuracy of recommendations. There are various types of recommendation systems, such as contentbased, neighborhood-based, matrix factorization and deep-learning-based systems. However, most of these systems are considered black boxes, since they do not offer transparency to the recommendation process, which makes it difficult for users to trust the recommendations that are presented to them. In this sense, providing interpretable recommendations tends to increase user confidence and satisfaction with the system. The use of explanations in recommendation systems has shown to be a promising area of research, but only a few works have explored the use of context as a way of generating explanations. Given this scenario, this project aims to propose a method that generates interpretable recommendations using contextual information. The obtained results showed that HINCARS had similar performance than a state-of-the-art recommendation method. (AU)

• FAPESP's process: 18/04651-0 - Generating explanations in recommender systems based on matrix factorization techniques using context
• Grantee: Vítor Rodrigues Tonon
• Support Opportunities: Scholarships in Brazil - Master