基于矩阵聚类的电子商务网站个性化推荐系统

提出一种基于“矩阵聚类”的电子商务网站个性化推荐系统,通过分析Web server日志文件中的访问页面序列行为数据,构建较高购买者的顾客行为的矩阵模型;并使用一种新型的“矩阵聚类”算法挖掘潜在购买者与较高购买者的相似特征,从而帮助顾客发现他所希望购买的产品信息,用于提高实际购买量．该技术特别适合于目前大型的电子商务网站,实验数据表明,该系统是高效并可广泛使用．

Personalized Recommender Systems Based on "Matrix Clustering" for E-commerce

In this paper we propose an alternative Personalized Recommender Systems Based on "Matrix Clustering" for E Commerce, the importance goals of recommender system for E commerce is to increase sales of existing produces by matching customers to the products that will be most likely to purchase. when Visitors navigate though a company`s web site, their interactions are captured in web logs, and the order in which visitors choose to view pages indicates their steps through the buying process. And the similarities in navigational behavior of top selling visitors can help to increase turnover. Matrix clustering is a new data mining method which extracts a dense sub matrix from a large sparse binary matrix. new recommender system technologies can quickly produce high quality recommendations. even for very Large scale,sparsity data .It shows that effecieacy and wide applicability can be a suitable technique for recommender system on E commerce site.