Recommendation System for Mobile Applications Tour Guide and Travel Services using Demographic Filtering and Content-Based Filtering Methods based on Android

Abstract

This research aims to build a tourism service recommendation system by providing recommendations for tour guides and tourism destinations using machine learning recommendation system methods. The recommendation system method in this research uses the demographic filtering method to recommend tour guides using a model with three stages, namely filtering, scoring and sorting the tour guide data category. At the scoring stage, it is carried out using a decision-making system method using the Simple Additive Weighting method to weight the tour guide value with the weighting value of each criterion in the tour guide data. This research also applies a content-based filtering method using a model with two stages, namely training and recommendation. The training stage uses tf-idf weighting and the recommendation stage uses cosine similarity to recommend tourism spots based on the similarity of metadata for each item. In using the recommendation system, the data will focus on collecting tour guide data and data on tourist attractions selected by the user. This data will be processed by a recommendation system using demographic filtering and content-based filtering methods. Through the Simple Additive Weighting method in demographic filtering and tf-idf weighting with consistent similarity in content-based filtering, this research found personalized recommendation results so that the level of accuracy of the recommendation results becomes more personal and accurate. The results of this research provide a recommendation system for tourist guides and tourist attractions which is implemented into an Android-based mobile application that can be used to meet tourists' needs.