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MULTITOUR: A multiple itinerary tourists recommendation engine

Publication Type : Journal Article

Source : Electronic Commerce Research and Applications, 2020

Url :

Campus : Amaravati

School : School of Computing

Verified : No

Year : 2022

Abstract : The importance of tourism in today’s world is immense as it is a big source of revenue and employment generation for a country. Tourists face a variety of challenges during planning of their itinerary as well as in the selection of appropriate tour packages which consist of multiple itineraries in terms of their interests and different constraints. In order to overcome these challenges, in this work we propose an algorithm called MULTITOUR, for recommending multiple itineraries based on the tourist’s interest, popularity of itineraries and the cost associated with these itineraries which is derived from real-life travel sequences of tourist using the geo-tagged photos. The MULTITOUR algorithm can be further extended when a tourist wishes to visit unfamiliar places. Using the Flickr dataset, we have derived the similar user characteristics for recommending the multiple itineraries. The experimental results indicate that the MULTITOUR algorithm out-performs in terms of tour Precision, Recall, F1-Score, accuracy, tour popularity, interest of the tourist and the number of itineraries recommended as compared to the baseline algorithms.

Cite this Research Publication : J.L. Sarkar, A. Majumder, C.R. Panigrahi & S. Roy, (2020). MULTITOUR: A multiple itinerary tourists recommendation engine. Electronic Commerce Research and Applications. DOI:40.100943. 10.1016/j.elerap.2020.100943

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