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Temporal-based approach to solve item decay problem in recommendation system


Citation

Al-Qasem, Al-Hadi Ismail Ahmed and Mohd Sharef, Nurfadhlina and Sulaiman, Md. Nasir and Mustapha, Norwati (2018) Temporal-based approach to solve item decay problem in recommendation system. Advanced Science Letters, 24 (2). pp. 1421-1426. ISSN 1936-6612; ESSN: 1936-7317

Abstract / Synopsis

The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). Recently, the temporal based factorization approaches have been used to solve the sparsity problem, but these approaches have a weakness in terms of learning the popularity decay of items during the long-term which lowers the prediction accuracy of the CF technique. The LongTemporalMF approach has been proposed to solve these problems. The x-means algorithm and the bacterial foraging optimization algorithm have been integrated within the LongTemporalMF approach to generate and optimize the genres weights which are integrated with the factorization features and the long-term preferences in terms of personality. The experimental results show that the LongTemporalMF approach has the accurate prediction performance compared to the benchmark approaches.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1166/asl.2018.10762
Publisher: American Scientific Publishers
Keywords: Bacterial foraging; Clustering; Collaborative filtering; Data sparsity; Matrix factorization; Recommendation system
Depositing User: Nabilah Mustapa
Date Deposited: 13 Aug 2018 11:16
Last Modified: 13 Aug 2018 11:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1166/asl.2018.10762
URI: http://psasir.upm.edu.my/id/eprint/64654
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