Coffee Recommender System Using Content-based Filtering

Abstract

Coffee is a type of beverage that is brewed and made from roasted seeds, or beans from coffee plants. Despite this, there are many coffee drinkers and non-coffee drinkers who do not know much about coffee knowledge. Besides that, there were many cafes that were being affected by the pandemic which caused a business drop. In addition, there are many new cafe owners who do not know much about coffee beans that are suitable to serve their customers. There is a need for a digital method to assist coffee drinkers and café owners either new or old, to receive recommendation about coffee beans that can serve the preferences of avid coffee enthusiasts or specialty coffee service provider. It is important to develop such a recommender system due to the overwhelming variety of coffee available. The goal of this project is to create a recommender system that informs coffee drinkers about various coffee bean varieties based on their personal preferences in coffee and provide a list of cafes that serve speciality coffee in Selangor focusing on Bandar Baru Bangi, Kajang and Serdang. The coffee and beverages that met the users' taste requirements were categorized and presented using this approach. A content-based filtering method that compares items depending on the user's preferences is used to generate the suggestion. In this project, a modified waterfall model of System Development Life Cycle was used to develop the prototype. This system was tested using functionality testing and accuracy testing and showed positive test results and the algorithm of content-based filtering was applied successfully. For future work, further development can be made to propose cafés instead by narrowing down the availability of preferred coffee beans at specific cafes. The recommendation method can also be fine-tuned by exploring the expert system approach in recommendation.