一种基于用户情境聚类的个性化推荐算法

面向基于情境感知的推荐问题，提出一种基于用户情境聚类的个性化推荐算法。该算法利用情境预过滤的思想，首先运用模糊聚类的方法对历史数据集中用户的情境进行聚类，构造与当前用户情境相似度较高的用户集合，再与传统的基于用户的协同过滤算法相结合进行个性化推荐。实验采用公开数据集，结果表明该算法在多维情境信息条件下可用，并且推荐准确度要高于传统协同过滤算法，在聚类粒度不同的情况下对推荐结果也会产生不同的影响。

A Personalized Recommendation Algorithm Based on User Context Clustering

Towards the problem of recommendation based on context-aware,propose a personalized recommendation algorithm based on user context clustering. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users＆#39; context in history data set first to construct the user set which is similar with current user context,and then combined with user-based collabora-tive filtering algorithm for personalized recommendation. The proposed methodology is tested using public datasets and the results show that it can be used in multidimensional context information. The recommendation precision is higher than traditional collaborative filtering algorithm. And find the change of the cluster size has an impact on the recommendation result.