{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T22:47:19Z","timestamp":1779058039279,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Big Data is changing how organizations conduct operations. Data are assembled from multiple points of view through online quests, investigation of purchaser purchasing conduct, and then some, and industries utilize it to improve their net revenue and give an overall better experience to clients. Each of these organizations must figure out how to improve the general client experience and meet every client\u2019s novel necessities, and big data helps with this cycle. Through the utilization and reviews of Big Data, travel industry organizations can study the inclinations of more modest portions of their intended interest group or even about people in some cases. In this paper, a Crow Search Optimization-based Hybrid Recommendation Model is proposed to get accurate suggestions based on clients\u2019 preferences. The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows\u2019 assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58.5%, 27%, 24.5%, and 25.5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms.<\/jats:p>","DOI":"10.3390\/info13020070","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T23:02:06Z","timestamp":1643497326000},"page":"70","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Exploring New Vista of Intelligent Recommendation Framework for Tourism Industries: An Itinerary through Big Data Paradigm"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5152-6438","authenticated-orcid":false,"given":"Manash","family":"Sarkar","sequence":"first","affiliation":[{"name":"Atria Institute of Technology, Bengaluru 560024, Karnataka, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arup","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Manipal University, Jaipur 303007, Rajasthan, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maroi","family":"Agrebi","sequence":"additional","affiliation":[{"name":"LAMIH UMR CNRS 8201, Department of Computer Science, Universit\u00e9 Polytechnique Hauts-de-France, 59313 Valenciennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hameed","family":"AlQaheri","sequence":"additional","affiliation":[{"name":"Department of Information System and Operation Management, College of Business Administration, Kuwait University, Safat 13060, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","first-page":"37","article-title":"A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations","volume":"6","author":"Verma","year":"2019","journal-title":"Int. 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