Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized traveler assistance (LAPTA), a system which integrates user preferences and the global positioning system (GPS) to generate personalized and location-aware recommendations. That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings. Specifically, LAPTA separates the data obtained from Google locations into name and category tags. After the data separation, the system fetches the keywords from the user’s input according to the user’s past research behavior. The proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized suggestions. The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability. The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.