Coffee shop recommendation system using an item-based collaborative filtering approach

• Main Authors: Renita Astri, Ahmad Kamal Ariffin Mohd Rus, Suaini Sura
• Format: Conference or Workshop Item
• Language: English; English
• Published: 2022
• Subjects: QA1-43 General
• Online Access: https://eprints.ums.edu.my/id/eprint/38452/1/ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/38452/2/FULLTEXT.pdf; https://eprints.ums.edu.my/id/eprint/38452/; https://ieeexplore.ieee.org/abstract/document/9944403
• Institution: Universiti Malaysia Sabah

To inhibit the rate of transmission of the Covid- 19 virus, one of the efforts made by the Indonesian government is to impose a system of limiting social activities. Thus, resulting in changes in patterns and lifestyles in a short time. Including this "Coffee" activity. A large amount of time available due to WFH has also resulted in an increase in the number of coffee connoisseurs, including the existence of the coffee shop itself. This makes it difficult for coffee fans to choose which coffee shop is the right one to go to desire. So, a recommendation system is needed that aims to provide advice on which coffee shop to choose. The recommendation system is a system that helps users overcome overflowing information by providing specific recommendations for users and it is hoped that these recommendations can meet the wishes and needs of users. There are three types of recommendation systems based on the methods they use, namely collaborative filtering, content-based filtering, and hybrid. The method used is collaborative filtering is often used in recommendation systems. Collaborative filtering is divided into two parts, namely Item-based collaborative filtering and User-based collaborative filtering. This paper uses Item-based collaborative filtering which uses rating data between users to get recommendations. In this technique, each coffee shop that is rated by the user is checked with similar coffee shops, then combines these similar coffee shops into a list of recommendations. The test results show that the Item-based collaborative filtering method with an adjusted cosine similarity algorithm can display recommendations that are by the rating given by the customer.