A Comparative Analysis of Content-Based Filtering and TF-IDF Approaches for Enhancing Sports Recommendation Systems

Sport is one of the important factors for someone to maintain or improve their health. There are many purposes for a person to exercise. However, many people still find it difficult to determine the relevant type of exercise according to their preferences. Recommendation systems are now an important element in human life to provide relevant recommendations to users. The purpose of this research is to develop a sports recommendation system that can provide accurate types of sports recommendations to users and describe how the recommendation system can work in providing recommendations when cold-start problems and non-cold-start problems occur. The method used in this research is content-based filtering by applying Term Frequency - Inverse Document Frequency (TF-IDF) vectorization matrix and cosine similarity algorithm. When a new user logs in, the system first checks the user's preferences to determine whether a cold-start problem or non-cold-start problem occurs. When a cold-start problem occurs, TF-IDF will be used in providing recommendations to the user. Conversely, when a non-cold-start problem occurs, cosine similarity will be used. The results show that by using TF-IDF and cosine similarity, the system successfully provides relevant sports recommendations to users in both cold-start problem and non cold-start problem situations with an accuracy rate of 86.90%. The novelty of this research lies in the understanding of sports provided to users through sports-related journals. Through these journals, it can increase user satisfaction, trust, compliance, and educate users in running sports