Gym activities recommender system using content based filtering algorithm / Muhammad Siddiq Sa’idin

This Gym Activities Recommender System serves to upgrade gym sessions by developing custom workout suggestions suited for each user's tastes as well as fitness objectives. The majority of gym members including newcomers battle to find appropriate exercises because they lack directional support and experience exercise complexity. The absence of proper guidance leads users to experience diminished motivation and choose wrong exercises that results in futile workouts. The proposed recommendation system bases its operation on Content-Based Filtering (CBF) to process metadata from different gym exercises which produces personalized workout recommendations. User-provided fitness objectives along with choice of workout exercises and experience background help the system develop customized workout profiles. The system matches users with appropriate exercises based on two similarity calculation methods which include cosine similarity alongside TF-IDF (Term Frequency-Inverse Document Frequency). The research adopts a formal methodology which combines gym activity dataset compilation and systematic design of the system with algorithm development and performance assessment.