On the integration of Machine Learning algorithms and Operations Research techniques in the development of a hybrid Recommender System

Article type: Research Article

Authors: Giannopoulos, Panagiotis | Kournetas, Georgios | Karacapilidis, Nikos^*

Affiliations: Industrial Management and Information Systems Lab, MEAD, University of Patras, Rio Patras, Greece

Correspondence: [*] Corresponding author: Nikos Karacapilidis, Industrial Management and Information Systems Lab, MEAD, University of Patras, 26504 Rio Patras, Greece. E-mail: karacap@upatras.gr.

Abstract: Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

Keywords: Recommender systems, Machine Learning, multi-criteria decision analysis

DOI: 10.3233/IDT-200217

Journal: Intelligent Decision Technologies, vol. 15, no. 3, pp. 497-510, 2021