{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:47Z","timestamp":1754156867214,"version":"3.41.2"},"reference-count":45,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,7,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of \u201cbig data\u201d such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization\u2013incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-11-2021-0332","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T09:56:28Z","timestamp":1670579788000},"page":"407-426","source":"Crossref","is-referenced-by-count":1,"title":["Topic optimization\u2013incorporated collaborative recommendation for social tagging"],"prefix":"10.1108","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5583-4000","authenticated-orcid":false,"given":"Xuwei","family":"Pan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6234-4683","authenticated-orcid":false,"given":"Xuemei","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7016-1390","authenticated-orcid":false,"given":"Ling","family":"Ding","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"issue":"4","key":"key2024072308212601100_ref001","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1002\/asi.23736","article-title":"A survey on tag recommendation methods","volume":"68","year":"2017","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"key2024072308212601100_ref002","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"year":"2016","key":"key2024072308212601100_ref003","article-title":"Capturing semantic correlation for item recommendation in tagging systems"},{"first-page":"11","article-title":"Understanding navigability of social tagging systems","year":"2007","key":"key2024072308212601100_ref004"},{"first-page":"795","article-title":"Gaussian LDA for topic models with word embeddings","year":"2015","key":"key2024072308212601100_ref005"},{"key":"key2024072308212601100_ref006","unstructured":"Duan, J., Ai, Y. and Ii, X. 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