一种电子商务协同过滤推荐算法

A collaborative filtering recommendation algorithm e-commerce

依据当前社会发展趋势及电子商务发展要求,设计了一种以用户偏好挖掘为基础的电子商务协同过滤推荐算法。此算法将用户隐性及显性偏好知识,运用用户偏好挖掘技术进行深入挖掘剖析,促进以用户偏好知识的智能推荐及最近邻居社区的构建得以实现。从本次研究的实验结果显示,此种算法在预期效果上比较理想,对于协同过滤推荐的准确性和质量具有显著提升效果。

On the basis of the current trend of social development and the requirement of e-commerce development, we design a user preference mining based e-commerce collaborative filtering recommendation algorithm. This algorithm will be user＇s recessive and dominant knowledge, using the user preferences deeply analyzes mining mining technology, to promote to the user preference knowledge of intelligent recommendation and neighbor community building recently. From the experimental results of this research shows that this algorithm on the expected effect is more ideal, for collaborative filtering recommendation accuracy and quality have a significant boost effect.