MAPPLE CAFÉ: WEB-BASED RECOMMENDATIONS CAFE WITH COLLABORATIVE FILTERING AND LOCATION SEARCH-BASED APPROACH

Abstract

In response to the Covid-19 pandemic era, the "school from home" and "work from home" measures that been implemented have significantly changed activity patterns. Activity patterns that are normally diverse and unbound become monotonous and constrained. The phenomena of "work from café" has evolved in the middle of monotony, boredom, and tension as a result of changing legislation and activity patterns, as well as the continued development of the food and beverage industry sector. Since the pandemic, people have begun to make work from cafes as a habit. Despite the pandemic, there is still a significant demand for trying new cafes that cater to users' preferences. To meet this demand, this web application has been developed to provide cafe recommendations based on user preferences and search location. The website is designed to be user-friendly and efficient, so it making easy for users to discover new cafes that meet their criteria. With its sophisticated location-based recommendation algorithm and user-friendly interface, this website is a valuable tool for anyone looking to discover new and trending cafes that cater to their personal preferences. Whereas the goal from the developer is to build a website that provides cafe recommendations to users based on their location input and personal preferences. The website will utilize a location-based recommendation approach, which takes into account the proximity of the cafes to the user's set location, as well as the preferences of similar users. The first step will be to gather data about cafes, their locations, and, as well as user preferences. The data will then be preprocessed, and a suitable recommendation algorithm, such as Collaborative Filtering, will be chosen and implemented.