Abstract
The travel industry is both a significant industry and a well-known recreation movement embraced by millions throughout the planet. One great errand for travelers is to plan and timetable visit schedules that involve the various dazzling Locations of Interest (LOIs) dependent on the extraordinary inclinations of a traveler. The mind-boggling errand of visit agenda proposal is additionally confounded by the need to fuse distinct genuine imperatives like restricted time for visiting, unsure traffic conditions, severe climate, group trips, lining times, and overcrowding. In this learning, we direct thorough writing examination of studies on visit schedule suggestions and present an overall scientific classification for visiting related examination. We will cover the Location of Interest (LOI) and sequence of LOIs studies that have been done to improve the traveling experiences of users. We talk about the whole cycle of visit schedule suggestion research covering: (i) information assortment and kinds of datasets; (ii) issue plans and suggested calculations/frameworks for individual travelers, gatherings of travelers, and different genuine contemplation; (iii) assessment strategies for looking at a visit to a recommended LOI; (iv) assessment strategies for comparing trip planned route recommendation algorithms; and (v) upcoming bearings and open issues in LOI and trip planned route recommendation research.
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Abbas, R. et al. (2023). Trip Recommendation Using Location-Based Social Network: A Review. In: Abd Elaziz, M., Medhat Gaber, M., El-Sappagh, S., Al-qaness, M.A.A., Ewees, A.A. (eds) International Conference on Artificial Intelligence Science and Applications (CAISA). CAISA 2022. Advances in Intelligent Systems and Computing, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-031-28106-8_8
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