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Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic \u2018features of attractions\u2019 to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.<\/jats:p>","DOI":"10.1177\/0165551521999801","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T04:40:04Z","timestamp":1617943204000},"page":"302-318","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Personalised attraction recommendation for enhancing topic diversity and accuracy"],"prefix":"10.1177","volume":"49","author":[{"given":"Yuanyuan","family":"Lin","sequence":"first","affiliation":[{"name":"School of Business Administration, Nanjing University of Finance &amp; Economics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4995-1002","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Southeast University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3738-0030","authenticated-orcid":false,"given":"Wei","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Southeast University, China"}]},{"given":"Yifei","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Southeast University, China"}]}],"member":"179","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tourman.2018.03.009"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/su11020323"},{"key":"e_1_3_2_4_2","first-page":"106","volume-title":"2018 IEEE international conference on progress in informatics and computing","author":"Qi Q","unstructured":"Qi Q, Cao J, Tan Y, et al. 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