基于改进权重增量Apriori算法的产品推荐方法

采用权重增量挖掘思想优化算法,为用户推荐个性化产品配置提供了有效的解决方案.方法主要主要分为3个部分,首先利用平台搭建起来的用户跟踪模块对用户行为进行跟踪和数据的收集;然后结合用户最近的行为习惯,使用基于权重增量的Apriori算法进行关联规则挖掘;最后根据挖掘出的结果完成产品推荐的过程.通过对挖掘算法的优化,大大提高了系统的运行效率和准确性,产品推荐随着用户行为的改变而改变,更加符合实际情况.实验结果表明,该算法可以有效解决产品推荐问题,相比于传统关联规则挖掘算法,准确率提高了4%.

Products Recommendation Based on Improved Weight Increment Apriori Analysis

In this paper, the weight of the incremental mining thinking optimization algorithm, for users to recommend personalized product configuration provides an effective solution. The method is mainly divided into three parts, first using the platform to build up user tracking module for tracking user behavior and collecting data; then combined with the user's behavior recently, the use of association rules mining based on the weight increment Apriori algorithm; final complete the product according to the recommended procedure to dig out results. By mining algorithm optimization, greatly improving the efficiency and accuracy of the product is recommended with the change in user behavior and changes in the system, more in line with the actual situation. Experimental results show that the algorithm can effectively solve the problem of product recommendation, compared to the traditional association rule mining algorithm, the accuracy is improved by 4%.