计算机科学 ›› 2014, Vol. 41 ›› Issue (Z11): 354-358.
李贵,陈召新,李征宇,韩子扬,孙平,孙焕良
LI Gui,CHEN Zhao-xin,LI Zheng-yu,HAN Zi-yang,SUN Ping and SUN Huan-liang
摘要: 推荐系统中,基于聚类的协同过滤推荐算法利用K-means等算法对用户和物品进行聚类,聚类结果里用户或物品只能属于一个类别,然而在实际应用中,一个用户可以有多种兴趣,一个物品也可以属于多个类别。针对上述问题,提出了一种基于谱聚类群组发现的算法,该算法通过谱聚类和C-means聚类得到用户和物品相似度较高的群组以及用户和物品归属于群组的隶属度矩阵,而且用户或物品可以属于多个群组。通过计算用户在各个群组中对物品的偏好值,并结合用户和物品在群组里相应的隶属度来预测用户对物品最终的偏好值,生成对用户的Top-N推荐结果。实验结果表明,与以往推荐算法相比,本方法在降低了数据稀疏性的同时提高了推荐结果的准确率和召回率。
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