Enhancing multi-aspect collaborative filtering for personalized recommendation

Citation

Khairudin, Nurkhairizan and Mohd Sharef, Nurfadhlina and Mustapha, Norwati and Mohd Noah, Shahrul Azman (2018) Enhancing multi-aspect collaborative filtering for personalized recommendation. In: 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP'18), 26-28 Mar. 2018, Le Méridien Kota Kinabalu, Sabah, Malaysia. (pp. 91-96).

Abstract

Most existing Collaborative Filtering (CF) approach relies on single overall ratings assigned to items. However, to precisely understand users' behaviours, sometimes this rating alone is not adequate. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of the item. This situation happened due to the emphasis on aspects may be different among users and will affect a user's final decisions. Therefore, in this paper, we proposed the multi-aspect tensor factorization (MATF) to enhance the predictive accuracy of multi-aspect recommendation by using Tensor Factorization. The evaluation shows that the proposed model outperforms various well-known existing techniques on both single and multi-criteria recommendation.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/INFRKM.2018.8464760
Publisher:	IEEE
Keywords:	Collaborative filtering; Tensor factorization; Multi-aspect
Depositing User:	Nabilah Mustapa
Date Deposited:	12 Jun 2019 02:05
Last Modified:	25 May 2020 01:49
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/INFRKM.2018.8464760
URI:	http://psasir.upm.edu.my/id/eprint/68926
Statistic Details:	View Download Statistic

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