An efficient approach for improving the predictive accuracy of multi-criteria recommender system
Recommender Systems are useful information filtering tools that have reduced information overload over the web. Collaborative filtering (CF) is one of the extensively used recommendation techniques. Traditional CF captures user-item ratings in a two-dimensional rating matrix which does not sufficien...
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Published in | International journal of information technology (Singapore. Online) Vol. 16; no. 2; pp. 809 - 816 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Singapore
Springer Nature Singapore
2024
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Abstract | Recommender Systems are useful information filtering tools that have reduced information overload over the web. Collaborative filtering (CF) is one of the extensively used recommendation techniques. Traditional CF captures user-item ratings in a two-dimensional rating matrix which does not sufficiently convey user preferences. Ratings based on several criteria are incorporated into CF to develop multi-criteria recommender systems (MCRS). MCRS are more efficient and cater to the users’ needs with more satisfaction. However, there are certain issues like multidimensionality, sparsity, and cold start associated with MCRS. This paper aims to study the MCRS and investigate efficient solutions for existing issues. In this direction, we proposed a modified similarity measure that improves the accuracy of neighborhood generation and rating prediction. In the proposed approach, the users are clustered based on multi-criteria ratings, which reduces the data sparsity and multidimensionality issues in MCRS. The supremacy of the proposed approach is verified by conducting experiments on a benchmark data set and evaluating the performance using some standard evaluation measures. |
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AbstractList | Recommender Systems are useful information filtering tools that have reduced information overload over the web. Collaborative filtering (CF) is one of the extensively used recommendation techniques. Traditional CF captures user-item ratings in a two-dimensional rating matrix which does not sufficiently convey user preferences. Ratings based on several criteria are incorporated into CF to develop multi-criteria recommender systems (MCRS). MCRS are more efficient and cater to the users’ needs with more satisfaction. However, there are certain issues like multidimensionality, sparsity, and cold start associated with MCRS. This paper aims to study the MCRS and investigate efficient solutions for existing issues. In this direction, we proposed a modified similarity measure that improves the accuracy of neighborhood generation and rating prediction. In the proposed approach, the users are clustered based on multi-criteria ratings, which reduces the data sparsity and multidimensionality issues in MCRS. The supremacy of the proposed approach is verified by conducting experiments on a benchmark data set and evaluating the performance using some standard evaluation measures. |
Author | Anwar, Khalid Zafar, Aasim Iqbal, Arshad |
Author_xml | – sequence: 1 givenname: Khalid orcidid: 0000-0002-7953-7200 surname: Anwar fullname: Anwar, Khalid email: khalid35amu@gmail.com organization: Department of Computer Science, Aligarh Muslim University, School of Computer Science Engineering and Technology, Bennett University – sequence: 2 givenname: Aasim surname: Zafar fullname: Zafar, Aasim organization: Department of Computer Science, Aligarh Muslim University – sequence: 3 givenname: Arshad surname: Iqbal fullname: Iqbal, Arshad organization: K.A. Nizami Center for Quranic Studies, Aligarh Muslim University |
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Cites_doi | 10.1007/s41870-018-0202-4 10.1016/j.heliyon.2023.e18183 10.1109/IISA.2013.6623719 10.1201/9780367423926-10 10.1016/j.knosys.2013.11.006 10.1007/s41870-023-01158-1 10.1109/MIS.2011.33 10.1504/IJRIS.2020.106803 10.1007/s41870-023-01205-x 10.1142/S0218348X23401497 10.1007/s41870-022-01113-6 10.1007/s11042-017-4924-2 10.54216/IJNS.200111 10.1109/TNSE.2023.3270910 10.1504/IJIIDS.2020.109457 10.1016/j.eswa.2021.114868 10.1016/j.procs.2018.04.190 10.31577/cai 10.1016/j.ins.2014.09.012 10.1007/s41870-022-00858-4 10.2139/ssrn.3356349 10.1016/j.knosys.2014.01.006 10.1007/s00500-014-1475- 10.1007/978-0-387-85820-3_24 10.1109/ICDMW.2018.00161 10.1016/j.asoc.2021.107782 10.1016/j.knosys.2020.105756 10.1016/j.elerap.2016.12.005 10.1016/j.elerap.2015.08.004 10.47164/ijngc.v13i3.820 10.1109/ICDABI56818.2022.10041638 10.1145/1559845.1559923 |
ContentType | Journal Article |
Copyright | The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
Copyright_xml | – notice: The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Title | An efficient approach for improving the predictive accuracy of multi-criteria recommender system |
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