Generating explanations in recommender systems based on matrix factorization techniques using context

• Grant number: 18/04651-0
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2018
• End date: February 02, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Solange Oliveira Rezende
• Grantee: Vítor Rodrigues Tonon
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Users face difficulties to choose products and services on the Web because of the wide range of choices. In this context, the recommendation systems aim to help users identify items of interest in a set of options. Traditional approaches focus on recommending more relevant items to individual users, not taking into account the users' context. However, in many real-world applications, using contextual information is also important. For that, context-aware recommendation systems are used, since there are several studies that indicate that the use of such contextual information can improve recommendations. Among the traditional and context-aware recommendation systems, there are those that use matrix factorization techiniques. These techniques have become popular because they combine good scalability with good accuracy, and offer the flexibility to model various real-world situations. However, they are models of machine learning that do not yet offer transparency in the recommendation process, making it difficult for users to trust in the presented recommendations. In this sense, providing explanations for the generated recommendations tends to increase the user's confidence and satisfaction with respect to the system. So, this project aims to propose methods that use contextual informations to create explanations for the recommendations generated by recommendation systems based on matrix factorization. It is expected that the generated explanations increase user's satisfaction. (AU)

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

SUNDERMANN, CAMILA VACCARI; DE PADUA, RENAN; TONON, VITOR RODRIGUES; DOMINGUES, MARCOS AURELIO; REZENDE, SOLANGE OLIVEIRA; OLIVEIRA, PM; NOVAIS, P; REIS, LP. A Context-Aware Recommender Method Based on Text Mining. PROGRESS IN ARTIFICIAL INTELLIGENCE, PT II, v. 11805, p. 12-pg., 2019-01-01. (18/04651-0)

SUNDERMANN, CAMILA VACCARI; DE PADUA, RENAN; TONON, VITOR RODRIGUES; MARCACINI, RICARDO MARCONDES; DOMINGUES, MARCOS AURELIO; REZENDE, SOLANGE OLIVEIRA. A context-aware recommender method based on text and opinion mining. EXPERT SYSTEMS, v. 37, n. 6, SI, AUG 2020. (16/17078-0, 18/04651-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

TONON, Vítor Rodrigues. Generating interpretable recommendations in recommender systems using context. 2021. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.