Hangout Places Recommendation System Using Content-Based Filtering and Cosine Similarity Methods - Abdul Raihan; Ahmad Ibrahim A.M; Alfian Akbar Gozali (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2024)
Coffee shops are becoming the new normal for friends and coworkers to hang out. Selecting the ideal location to hang out can be exceedingly difficult. There are too many choices, and it can be difficult to know where to begin. Based on this problem, a web application that responds to the growing need for an easy method of finding local hangouts is named Nongkies. With a focus on social interaction and exploration, this platform uses a recommender system to find cafes, restaurants, and entertainment venues easily. Key features include location-based search, category, and details places. Extensive testing has confirmed the reliability of Nongkies, offering user-friendly and accurate search results. This system is a website app that suggests places to users based on their preferences. This application was developed using the cosine similarity method, which is a systematic approach that uses a similar method based on cosine angles. Content that is less alike gets lower rankings, while more similar content gets the highest rankings in recommendations. Moreover, this app helps users find local hangouts and directions to those locations, especially university students, and the selection of places to socialize has a significant effect on students' learning experiences.