Conversational Recommender System for Impromptu Tourists to Recommend Tourist Routes Using Haversine Formula

Abstract

In this paper, we use two terms to describe tourists, i.e. planned tourists and impromptu tourists. Planned tourists are tourists who intentionally travel. Meanwhile, impromptu tourists are those who accidentally become tourists because they are in a new area for an activity. Previously, tourists who were going to travel usually relied on the services of travel agents to get recommendations for tourist attractions, different from impromptu tourists this was not done before. Impromptu tourists sometimes do not have much time to carry out tourism activities so that impromptu tourists only visit the closest tourist attractions from their location. Lack of experience in a new area and only relying on information on the internet makes it difficult for tourists to find tourist attractions based on their preferences. One solution to this problem is that a system is needed that can recommend tourist attractions in terms of distance by considering tourist preferences. In this study, we developed a conversational recommender system (CRS) to obtain user preferences. For the method we use the Haversine Formula to calculate the distance. The results of this study are a web application that recommends tourist attractions and routes to several tourist attractions, which can be done at one time. Based on the evaluation of the time complexity in the route search, linear complexity is obtained which shows good performance with optimal conditions.