{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:38:26Z","timestamp":1773383906108,"version":"3.50.1"},"reference-count":48,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T00:00:00Z","timestamp":1589760000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2021,6,7]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors\u2019 work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors\u2019 work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-10-2019-0584","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T07:43:39Z","timestamp":1589787819000},"page":"1268-1286","source":"Crossref","is-referenced-by-count":13,"title":["Research on power-law distribution of long-tail data and its application to tourism recommendation"],"prefix":"10.1108","volume":"121","author":[{"given":"Xiang","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yaohui","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Luo","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2020,5,18]]},"reference":[{"key":"key2021060810250132600_ref001","doi-asserted-by":"crossref","first-page":"6332","DOI":"10.1038\/s41598-018-24456-2","article-title":"Popularity and novelty dynamics in evolving networks","volume":"8","year":"2018","journal-title":"Scientific Reports"},{"key":"key2021060810250132600_ref003","first-page":"459","article-title":"Beyond rating prediction accuracy: on new perspectives in recommender systems","year":"2013"},{"key":"key2021060810250132600_ref002","unstructured":"Adamopoulos, P. and Tuzhilin, A. (2011), \u201cOn unexpectedness in recommender systems: or how to expect the unexpected\u201d, Workshop on Novelty and Diversity in Recommender System, pp. 11-18."},{"issue":"4","key":"key2021060810250132600_ref004","first-page":"54","article-title":"On unexpectedness in recommender systems: or how to better expect the unexpected","volume":"5","year":"2014","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"6","key":"key2021060810250132600_ref005","first-page":"130","article-title":"Diameter of the world-wide web","volume":"401","year":"1999","journal-title":"Nature"},{"key":"key2021060810250132600_ref006","doi-asserted-by":"crossref","unstructured":"Berjani, B. and Strufe, T. (2011), \u201cA recommendation system for spots in location-based online social network\u201d, SNS'11: Proceedings of the 4th Workshop on Social Network Systems, p. 4.","DOI":"10.1145\/1989656.1989660"},{"key":"key2021060810250132600_ref007","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.eswa.2017.10.049","article-title":"Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos","volume":"94","year":"2018","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"key2021060810250132600_ref008","first-page":"1","article-title":"Review on tourism recommendation system","volume":"44","year":"2017","journal-title":"Computer Science"},{"key":"key2021060810250132600_ref009","doi-asserted-by":"crossref","unstructured":"Charikar, M.S. (2002), \u201cSimilarity estimation techniques from rounding algorithms\u201d, Proceedings of the 34th Annual ACM Symposium on Theory of Computing, pp. 380-388.","DOI":"10.1145\/509907.509965"},{"key":"key2021060810250132600_ref010","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.inffus.2013.05.011","article-title":"User-adapted travel planning system for personalized schedule recommendation","volume":"21","year":"2015","journal-title":"Information Fusion"},{"key":"key2021060810250132600_ref011","first-page":"115","article-title":"Addressing cold start for next-song recommendation","year":"2016"},{"issue":"4","key":"key2021060810250132600_ref012","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1137\/070710111","article-title":"Power-law distributions in empirical data","volume":"51","year":"2009","journal-title":"SIAM Review"},{"issue":"3","key":"key2021060810250132600_ref013","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1080\/17538947.2017.1326535","article-title":"Personalized travel route recommendation using collaborative filtering based on GPS trajectories","volume":"11","year":"2018","journal-title":"International Journal of Digital Earth"},{"issue":"1","key":"key2021060810250132600_ref014","first-page":"58","article-title":"Social dilemmas in an online social network: the structure and evolution of cooperation","volume":"371","year":"2007","journal-title":"Physics Letters A"},{"key":"key2021060810250132600_ref015","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.pmcj.2016.04.004","article-title":"Personalization and context-awareness in social local search: state-of-the-art and future research challenges","volume":"38","year":"2017","journal-title":"Pervasive and Mobile Computing"},{"key":"key2021060810250132600_ref016","first-page":"7821","article-title":"Community structure in social and biological networks","year":"2002"},{"key":"key2021060810250132600_ref017","doi-asserted-by":"crossref","first-page":"012019","DOI":"10.1088\/1757-899X\/261\/1\/012019","article-title":"Research on long tail recommendation algorithm","volume":"261","year":"2017","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"key2021060810250132600_ref018","doi-asserted-by":"crossref","unstructured":"Indyk, P. and Motwani, R. 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