Title: SCOL: similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Authors: Nassira Chekkai; Ilyes Chorfi; Souham Meshoul; Badreddine Chekkai; Didier Schwab; Mohamed Belaoued; Amel Ziani

Addresses: MISC Laboratory, Constantine 2 University, Ali Mendjeli Campus, Constantine, 25000, Algeria ' Aix Marseille University, Marseille, 13007, France ' CCIS Research Center, Princess Noura Bint Abdulrahman University, Riyadh, KSA ' Abdelhamid Mehri Constantine 2 University, Constantine, 25000, Algeria ' LIG-GETALP, Université Grenoble Alpes, 38058, France ' LIRE Laboratory, Constantine 2 University, Constantine, Algeria ' LRI Laboratory, Computer Science Department, Annaba, Algeria

Abstract: Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.

Keywords: collaborative filtering; recommender systems; cold start problem; social network; graph theory; credibility; correlation similarity.

DOI: 10.1504/IJRIS.2020.105006

International Journal of Reasoning-based Intelligent Systems, 2020 Vol.12 No.1, pp.34 - 50

Received: 06 Apr 2018
Accepted: 30 Apr 2019
Published online: 10 Feb 2020 *

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