双向聚类迭代的协同过滤推荐算法

A Collaborative Filtering Recommendation Algorithm Based on Iterative Bidirectional Clustering

协同过滤是电子商务推荐系统中广泛采用的技术,然而数据稀疏性会影响协同过滤的推荐质量。针对数据稀疏问题提出一种双向聚类迭代的协同过滤推荐算法,对初始得到的用户聚类和项目聚类进行交叉迭代调整,使得聚类簇达到较为稳定的状态。调整后聚类簇的内聚性更强,类之间的区分度更大。实验表明,在调整后的聚类簇中查找邻居将更加准确,可以有效解决数据稀疏问题的影响,有利于提高推荐的准确性。

Collaborative filtering is widely applied in E-Commerce recommendation system. However, data sparcity affects the accuracy of prediction and results in poor recommendation. To address this problem, a novel collaborative filtering algorithm is presented based on the iterative bidirectional clustering method. It works on the initial user clusters and the item clusters, adjusting the two groups of clusters into the stable status by the cross iteration so that the distances within the cluster are much smaller whereas the distances between the clusters are even bigger. The experiments illustrate that the adjusted clusters facilitate a more accurate neighbor search, indicating an efficient solution to the data sparcity and better recommendation quality.