一种改进项目多属性类别划分的推荐算法.

The traditional measurement method of similarity ignores the project of multi-attribute category differences. To avoid this problem, this paper proposed a recommendation algorithm for improving project multi-attribute classification. Firstly, it used the project-user membership matrix to explore the affiliation and created a similar neighbor FP-Tree to extract the nearest neighbor set. Then it analyzed the common item similarity between users and the difference of the project multi-attribute classification, and used the weight factor to combine the common project with multi-attribute classification. It constructed the CNB model to measure the similarity degree of neighbors. Finally, it sorted the similar users in descending order to obtain more accurate similar users and complete the recommendation work. In virtue of the medical dataset, it verified the effectiveness of the proposed algorithm. The experimental results show that the recommendation accuracy of time complexity and mean of average accuracy have been improved. [ABSTRACT FROM AUTHOR]