Vol.31, No.1, 2022, pp.661-675, doi:10.32604/iasc.2022.020132
OPEN ACCESS
ARTICLE
A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations
  • Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour*
Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19328, Jordan
* Corresponding Author: Qusai Y. Shambour. Email:
Received 09 May 2021; Accepted 10 June 2021; Issue published 03 September 2021
Abstract
Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.
Keywords
Recommender systems; collaborative filtering; multi-criteria; implicit similarity; global reputation
Cite This Article
Hussein, A. H., Kharma, Q. M., Taweel, F. M., Abualhaj, M. M., Shambour, Q. Y. (2022). A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations. Intelligent Automation & Soft Computing, 31(1), 661–675.
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