Title: An evolutionary-based approach for providing accurate and novel recommendations

Authors: Chems Eddine Berbague; Hassina Seridi-Bouchelaghem; Karabadji Nour El-Islem; Symeonidis Panagiotis; Markus Zanker

Addresses: Electronic Document Management Laboratory (LabGED), Badji Mokhtar University, P.O. Box 12, 23000, Annaba, Algeria ' Computer Science Department, Badji Mokhtar University, Annaba, Algeria ' Higher School of Industrial Technologies, Annaba, Algeria ' Computer Science Department, Free University of Bolzano, Bolzano, Italy ' Computer Science Department, Free University of Bolzano, Bolzano, Italy

Abstract: For memory-based collaborative filtering, the quality of the target user's neighbourhood plays an important role for providing him/her with successful item recommendations. The existent techniques for neighbourhood selection aim to maximise the pairwise similarity between the target user and his/her neighbours, which mainly improves only the recommendation accuracy. However, these methods do not consider other important aspects for successful recommendations such as providing diversified and novel item recommendations, which also highly affect users' satisfaction. In this paper, we linearly combine two probabilistic criteria for selecting the right neighbourhood of a target user and provide him/her accurate, and novel item recommendations. The combination of these two probabilistic quality measures forms a fitness function, which guides the evolution of a genetic algorithm. For each target user, the genetic algorithm explores the user's whole search space and selects the most suitable neighbourhood. Thus, our approach makes a balance between the accuracy and the novelty of the provided item recommendations, as will be experimentally shown on MovieLens dataset.

Keywords: genetic algorithm; neighbourhood selection; novelty; diversity; relevancy; cold start problem.

DOI: 10.1504/IJBIDM.2022.124855

International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.2, pp.129 - 148

Received: 22 May 2020
Accepted: 29 Jan 2021

Published online: 11 Aug 2022 *

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