A Novel Personalized Recommendation Algorithm of Collaborative Filtering Based on RFM Model - Ning Zhang; Chongrui Fan; Yan Zhang (Dianxin kexue)
In order to improve the accuracy of recommendation, especially the matrix score of personalized recommendation technology is too spars, a new recommendation algorithm was proposed. The advantages of this algorithm were mainly embodied in the following aspects. Firstly, the improved algorithm with RFM model was used to select the original customer in some condition, making the recommended source of data more accurate and efficient. Secondly, in the improved algorithm the customer consumption history records were filled to the matrix to improve the consistency of the matrix of score. Thirdly, the traditional Pearson similarity calculation formula was improved to make the search of target users of similar neighbor more accurate. Then the simulation experiment was carried on by using the improved algorithm. It can be proved that the improved algorithm is better than the traditional one in accuracy. At last, the improved algorithm was applied to a recommendation system with personalized recommendation function. It was shown that the recommendation algorithm was efficient and valid.