A new item similarity based on α-divergence for collaborative filtering in sparse data Wang, Yong, Wang, Pengyu, Liu, Zhou and Zhang, Leo Yu 2021, A new item similarity based on α-divergence for collaborative filtering in sparse data, Expert Systems with Applications, vol. 166, doi: 10.1016/j.eswa.2020.114074. Attached Files Name Description MIMEType Size Downloads Title A new item similarity based on α-divergence for collaborative filtering in sparse data Author(s) Wang, Yong Wang, Pengyu Liu, Zhou Zhang, Leo Yu orcid.org/0000-0001-9330-2662 Journal name Expert Systems with Applications Volume number 166 Article ID 114074 Total pages 12 Publisher Elsevier Place of publication Oxford, Eng. Publication date 2021-03-15 ISSN 0957-4174 Keyword(s) similarity measure α-divergence Collaborative filtering Recommendation Management information system Summary In big data era, collaborative filtering as one of the most popular recommendation techniques plays an important role to promote the development of online trade. Similarity measurement is a core step in collaborative filtering as it not only determines the selection of neighbors but also has a decisive influence on the recommendation quality. However, most of existing similarity measures depend on the co-rated cases(i.e., cases where different users rated the same items or different items were rated by the same users), which usually leads to low data utilization and even poor recommendation results in a sparse dataset. To alleviate this problem, we proposed a new item similarity measure based on -divergence, which does the computation according to the probability density distribution of ratings and greatly reduces the dependence on co-rated cases. Furthermore, the presented item similarity measure also considers the impact of the absolute number of ratings and the proportion of co-rated cases on the computation results, which effectively improves the accuracy of recommendation. Experiments on three open datasets suggest that the proposed scheme has high prediction accuracy and good adaptability to sparse data. Therefore, it has high potential to be applied in recommender systems. Language eng DOI 10.1016/j.eswa.2020.114074 Indigenous content off Field of Research 01 Mathematical Sciences 08 Information and Computing Sciences 09 Engineering HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2020, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30144726 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 30 Oct 2020, 12:16:00 EST Sat, 31 Oct 2020, 04:01:24 EST Mon, 02 Nov 2020, 06:59:15 EST Wed, 04 Nov 2020, 05:05:28 EST Wed, 04 Nov 2020, 13:40:35 EST Wed, 04 Nov 2020, 13:43:22 EST Wed, 04 Nov 2020, 14:56:34 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 59 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Fri, 30 Oct 2020, 12:16:00 EST

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