基于用户兴趣模型聚类的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on Clustering of User Interest Model

随着电子商务的飞速发展,协同过滤推荐系统得到了广泛应用。本文针对传统协同过滤方法难以准确确定目标用户的最近邻居且推荐实时性能不高的问题,引入用户兴趣模型的概念并在此基础上给出一种基于用户兴趣模型聚类的协同过滤算法。实验结果表明,该算法可以提高最近邻居计算的准确性,提高推荐系统实时性能。

With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used. In this paper, to address the low accuracy of identifying nearest neighbors and low real-time performance of recommendation in traditional collaborative filtering algorithms, the concept of user interest model is introduced. Based on clustering of user interest model, a collaborative filtering recommendation algorithm is proposed. The experimental results suggest that this algorithm can efficiently improve the accuracy of computing nearest neighbor and improve the real-time performance of recommendation system.