Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering

With the rapidly growing amount of information available, the problem of information overload is always growing acute. Personalized recommendations are an effective way to get user recommendations for unseen elements within the enormous volume of information based on their preferences. The personalized recommendation system commonly used methods are content-based filtering, collaborative filtering and association rule mining. Unfortunately, each method has its drawbacks. This paper presented a personalized recommendation method combining the association rules mining and collaborative filtering. It used the association rules mining to fill the vacant where necessary. And then, the presented approach utilizes the user based collaborative filtering to produce the recommendations. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.