Swarm intelligence techniques in recommender systems - A review of recent research

Peška, Ladislav; Tashu, Tsegaye Misikir; Horváth, Tomáš

Abstract

One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them are Swarm Intelligence (SI) techniques, which are an emerging trend with various application areas. Although the interest in using Computational Intelligence in web personalization and information retrieval fostered the publication of some survey papers, these surveys so far focused on different application domains, e.g., clustering, or were too broadly focused and incorporated only a handful of SI approaches. This study provides a comprehensive review of 77 research publications applying SI in RS. The study focus on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported results. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this survey is the most comprehensive review of approaches using SI in RS. Therefore, we believe this review will be a relevant material for researchers interested in either of the domains.

• Journal Article Type: Review
• Acceptance Date: Apr 5, 2019
• Online Publication Date: Apr 14, 2019
• Publication Date: 2019-08
• Deposit Date: Mar 27, 2024
• Journal: Swarm and Evolutionary Computation
• Print ISSN: 2210-6502
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 48
• Pages: 201-219
• DOI: https://doi.org/10.1016/j.swevo.2019.04.003
• Keywords: Swarm intelligence, Particle swarm optimization, Ant colony optimization, Invasive weed optimization, Artificial bee colony, Recommender systems, Personalization
• Public URL: http://researchrepository.napier.ac.uk/Output/3577501