Collaborative Filtering Recommendation Algorithm Based on AdaBoost-Naïve Bayesian Algorithm

Abstract

With the rapid development of social network and E-commence, collaborative filtering (CF) has been widely applied and studied as an effective way to alleviate the pressure of information overloading. However, the CF-based algorithms often degrade significantly in their recommendation performance due to the sparse history rating data. In view of this sparse problem, this paper presented a collaborative filtering recommendation algorithm based on AdaBoost-Naïve Bayesian algorithm. In this model, we transform the traditional algorithms of predicting ratings directly to classify unrated data. Considering that Bayesian theory is a good machine learning method that is often used for text class. Considering that machine learning method can be used for text classification, we utilize it to learn user’s preferences for a certain item characteristic as a based classify. Ensemble learning, Adaboost, is also adopted to optimize and adjust adaptively the weights of the results of Bayesian classify. Finally, all of the predicted results of unrated data would be filled into the user-item matrix. The experimental results show that the sparseness of rating data has been alleviated and our proposed algorithm can improve the quality of recommender system.