A new point-of-interest approach based on multi-itinerary recommendation engine. (1st November 2021)

• Record Type: Journal Article
• Authors: Sarkar, Joy Lal; Majumder, Abhishek
• Is Part Of: Expert systems with applications. Volume 181(2021)
• Publication Date: 2021-11-01
• Subjects: Recommendation system -- Tourist -- Cost -- Itineraries -- POIs; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115026
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Recommend multiple itineraries by considering POI within POI. Maximizes the tourist's interest, tour popularity, and minimizes travel costs. Can be further extended when a tourist wishes to visit unfamiliar places. The PWP algorithm is designed to solve the Orienteering problem. Abstract: The significance of tourism in the globe today is enormous since it is a major source of income and jobs for a nation. Tourists are facing a range of difficulties as they select suitable tours, consisting of several itineraries in terms of their interests and distinct constraints. An itinerary consists of many Points of Interest (POIs) and a POI can further be splitted into several attractions which are named as POI within POI. For selecting the itinerary, the existing techniques use the characteristics of POIs. However, a POI consists of many attractions. Out of these, one dominating attraction's type is considered as POI type. This ignores the other type of attraction's present in that POI. It may cause improper selection of itineraries. Therefore, selection of itineraries by considering POI within POI is of great benefit. But, it is very challenging. For this task, we suggest an algorithm called PWP. It recommends multiple itineraries that are based on the interest of visitors, popularity of itineraries and the cost of itineraries. If a tourist wants to visit unknown areas, the PWP algorithm can be expanded further. We have taken the similar user's features to advise multiple itineraries using the Flickr dataset. The findings show that the proposed PWP algorithm out-performs the baseline algorithms in terms of real-life matrices and heuristic based metrics.