Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining

A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%.