A Point of Interest Recommendation Engine with an Integrated Approach

Abstract

Tourism is turning out to be a vital industry for a maximum of the economies, especially for non- industrialized nations, wherein it represents the principal supply of earnings. A POI Recommendation Engine predicts and gives personalized advice of a set of locations or entities based on the interests of a user. Recommendation engines are broadly used in tourism, but there is no unified framework model that takes in the geographic location of a user, review rating of the location, and the user's past behavior. This proposed system is built upon all these three components being mapped into an integrated approach to recommend a point of interest. The purpose of building this model is to upgrade recommendation approaches, which are mainly focused on key-word-dominant Internet Carrier search engines possessing deficient advice performance and heavy dependence on correct and complex queries from customers. We apply Hierarchal Agglomerative Clustering and Collaborative filtering as a source of getting information and recommendations on this tool. The outcome shows a specific set of locations suited along the geographical location of the user, review ratings of the location, and past behaviour of a user.