A deep learning based algorithm for multi-criteria recommender systems

Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria recommender systems proved to have more accurate recommendations compared to single-criterion recommender systems as multi-...

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Veröffentlicht in:Knowledge-based systems Jg. 211; S. 106545
1. Verfasser: Shambour, Qusai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 09.01.2021
Elsevier Science Ltd
Schlagworte:
Algorithms
Appreciation
Collaborative filtering
Computer vision
Criteria
Deep autoencoders
Deep learning
Experiments
Image processing
Learning
Machine learning
Multi-criteria recommender systems
Multiple criterion
Natural language processing
Object recognition
Pattern recognition
Recommender systems
Deep learning
Collaborative filtering
Multi-criteria recommender systems
Deep autoencoders
ISSN:0950-7051, 1872-7409
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Zusammenfassung:Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria recommender systems proved to have more accurate recommendations compared to single-criterion recommender systems as multi-criteria rating reflects the user appreciation of an item in terms of many aspects. On the another hand, deep learning techniques achieve promising performance in many research areas such as image processing, computer vision, pattern recognition and natural language processing. Recently, the application of deep learning in recommender systems have been frequently explored with encouraging results. Accordingly, this paper proposes a deep learning based algorithm for multi-criteria recommender systems in which deep autoencoders are employed to exploit the non-trivial, nonlinear and hidden relations between users with regard to multi-criteria preferences, and generate more accurate recommendations. Experiments on the Yahoo! Movies and TripAdvisor multi-criteria datasets show that the proposed algorithm prove to be very effective in terms of producing more accurate predictions compared with the state-of-the-art recommendation algorithms
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.106545