个性化推荐系统中相似模式聚类研究

Research on Similar Pattern Clustering in Personality Recommendation System

分析了目前在推荐系统中常用的用以计算访问行为相似程度的距离函数,发现它们仅是测定访问者对象在所有测试属性空间上的平均测定,而在属性集的子维空间上的相似模式并没有有效地挖掘出来。给出一种新的相似模式聚类算法,能高效地得到访问者对象在整个或者部分属性空间的相似访问行为模式。实验系统是高效的。

In the applications of recommendation systems in the E-commerce, sets, customers/clients with similar behavior need to be identified so that people can predict customers' interest and make quality recommendation, the key technology to produce high quality recommendation is define similarity among different objects by distances over either all or only a subset of the dimensions, some well-known distance functions are not always capture correlations among the object. The paper proposes a novel similar pattern clustering algorithm that can discover the pattern that exhibits a coherent pattern on a subset of dimensions. It shows that efficiency and wide applicability can be a suitable technique for recommender system on E-commerce site.