The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. (January 2023)

• Record Type: Journal Article
• Authors: Nilashi, Mehrbakhsh; Ali Abumalloh, Rabab; Samad, Sarminah; Minaei-Bidgoli, Behrouz; Hang Thi, Ha; Alghamdi, O.A.; Yousoof Ismail, Muhammed; Ahmadi, Hossein
• Is Part Of: Telematics and informatics. Volume 76(2023)
• Publication Date: 2023-01
• Subjects: Recommender Systems -- Multi-Criteria Collaborative Filtering -- Products Features -- Accuracy -- Text Mining; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2022.101919
• Languages: English
• ISSNs: 0736-5853

Abstract

Highlights: The impact of multi-criteria ratings on recommendation agents performance is investigated. LDA for feature extraction and EM and SOM for data clustering are used. Online customers' reviews from TripAdviosr are analysed. Sparsity issue was alleviated by the clustering techniques. The accuracy of CF recommender systems was improved by multi-criteria ratings. Abstract: Recommender Systems (RSs) have played an important role in online retailing portals and customers' decision-making processes. Recommender systems that are based on the conventional Collaborative Filtering (CF) approach rely on single customers' ratings on retailing websites. Multi-criteria CF (MCCF) approaches that rely on multi-aspects of the products have provided more reliable and effective recommendations on retailing websites. However, these approaches should be improved in terms of accuracy by solving sparsity issues and incorporating criteria ratings. In addition, most of the recommendation agents that are based on MCCF cannot learn automatically from the features of the products to model customers' preferences and generate accurate recommendations on retailing websites. Besides, although previous studies have utilized single and multi-criteria ratings in recommendation agents of tourism websites, still, if there is a lack of ratings of items, most of these systems will fail to generate accurate recommendations to users. In this research, we develop a new recommendation agent based on a MCCF approach to effectively improve the performance of previous recommendation systems for tourism websites. The results demonstrated that the method can predict the most relevant products to users, particularly when the dataset is sparse.