针对用户兴趣偏好多变问题,提出一种兴趣特征权重随时间而变化的迭代计算方法.构造了用户兴趣特征与主题类间的二部图关系,并在此基础上提出了一种基于主题的用户兴趣聚类算法(TBC),改变了聚类对象"非此即彼"的硬划分方式.该算法所形成的基于主题的用户兴趣域结构,不仅充分表达了用户的多域兴趣特征和域间主题的联系,还能适应用户兴趣变化.实验表明,TBC算法比传统的K-Means算法以及属于软划分方式的FCM聚类具有更好的用户兴趣划分效果,并且在个性化推荐服务中表现出更高的推荐质量和效率.%In order to solve the problem of users' preferences changing frequently, an iterative computing method was presented to gain the weights of users' preferences as time goes. A bipartite graph was constructed to show the relations of users' interests and topic classes. On this base, a novel topic-based clustering (TBC) algorithm was proposed to group the nearest neighbors according to users' interests, which had changed the usual hard partition method meaning “one or the other” for the clustering items. And the partitioned domains of users' interests based on multiple topics was also established by the algorithm, which not only fully profiled users' interests and the relations of topics indirectly reflected in different domains, but also could adaptively track the changes of users' interests. Experimental results show that TBC method has better declustering outcome of users' interests than both the traditional K-Means algorithm and FCM method belonged to the soft clustering, and the TBC algorithm also has higher recommendation quality and better efficiency in personalized recommender services.
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