Geo-based recommendation system utilising geo tagging and K-means clustering

다국어 초록 (Multilingual Abstract)

As technology advances, recommendation systems play an increasingly significant role in everyday life. Users today receive information efficiently and effectively through location-based recommender systems on their mobile devices. Geo-tagged data and the global positioning system are used to gather information about users in location-specific recommender systems. In this busy world, coffee is also a daily requirement. Therefore, we determine whether a particular population of individuals with mobile devices or other utility devices needs recommendations for coffee shops in a particular area. This was achieved by creating a Coffee Shop recommendation system, which uses geotagging to pinpoint the location dependent on latitude and longitude. In this article, we present a machine learning approach to assigning locations to coffee shops based on geo-based location suggestions. To determine the effectiveness of the coffee shop recommendation, a population-based zone-wise analysis was also conducted.