A collaborative recommender system based on user association clusters

The ever-increasing popularity of the Internet has led to an explosive growth of the sheer volume of information. Recommender system is one of the possible solutions to the information overload problem. Traditional item-based collaborative filtering algorithms can provide quick and accurate recommendations by building a model offline. However, they may not be able to provide truly personalized information. For providing efficient and effective recommendations while maintaining a certain degree of personalization, in this paper, we propose a hybrid model-based recommender system which first partitions the user set based on user ratings and then performs item-based collaborative algorithms on the partitions to compute a list of recommendations. We have applied our system to the well known movielens dataset. Three measures (precision, recall and F1-measure) are used to evaluate the performance of the system. The experimental results show that our system is better than traditional collaborative recommender systems.