Analyzing AprioriTID, Apriori Hybrid, and FP Growth for Association Rules in Movie Recommender Systems

This research investigates and compares the performance of three widely used association rule mining algorithms—FP-Growth, Apriori Hybrid, and AprioriTID in the domain of movie recommender systems. The study aims to assess the efficacy of these algorithms in generating meaningful association rules and subsequently providing personalized movie recommendations based on user preferences. The experimental methodology involves the utilization of a diverse dataset encompassing user ratings, genres, and historical viewing patterns. The implications of these results are discussed, providing a foundation for further research and advancements in the field of association rule mining for personalized content recommendations.