The Use of Multiple Criteria Decision Aiding Methods in Recommender Systems: A Literature Review

Author(s)

Pelissari, Renata ; Alencar, Paulo S. ; Ben Amor, Sarah ; Duarte, Leonardo Tomazeli ; Xavier-Junior, JC ; Rios, RA

• Document type: Journal article
• Source: INTELLIGENT SYSTEMS, PT I; v. 13653, p. 15-pg., 2022-01-01.

Abstract

Multiple Criteria Decision Making (MCDA) methods have been increasingly applied to improve recommendations when multiple criteria are considered in Recommender Systems (RSs). This study presents the preliminary results of a systematic literature review, following Kitchenham's guidelines, regarding the application of MCDA methods in RSs over the last two decades. Based on our findings, MCDA methods can be applied in two RS phases: the preference elicitation and the recommendation phases. In the former, RSs usually have a strong interaction with the user, which results in more personalized recommendations, ensuring higher user satisfaction and contributing to address the cold-start challenge in RSs. Regarding the recommendation phase, while most RSs are based on ranking approaches, there is a trend to apply sorting methods in order to avoid an additional step involving a filtering application that selects a subset of alternatives. Future research could focus on applying preference learning combined with MCDA methods for exploring improvements in prediction and recommendation phases, and also in quality and processing time. (AU)

• FAPESP's process: 20/01089-9 - Unsupervised signal separation: a study on the applicability of Generative Adversarial Networks and on nonlinear models based on the Choquet Integral
• Grantee: Leonardo Tomazeli Duarte
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program