计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 52-57.doi: 10.11896/j.issn.1002-137X.2017.12.010
臧雪峰,刘天琦,孙小新,冯国忠,张邦佐
ZANG Xue-feng, LIU Tian-qi, SUN Xiao-xin, FENG Guo-zhong and ZHANG Bang-zuo
摘要: 在大数据时代,为了满足用户的信息需求,个性化推荐系统得到了广泛应用。协同过滤是一种简单有效的推荐算法。然而,许多传统的相似度计算方法仅仅基于用户的共同评分值,且不适用于稀疏数据环境,因此提出了一种新的基于Bhattacharyya系数的相似度方法。该方法使用了所有用户对项目的评分信息,不仅可以通过用户的评分行为获得用户的相似兴趣特征,而且可以获得用户已评分物品之间的相关性;同时由于不同的用户有不同的评分习惯,新方法也考虑了每个用户的评分偏好。通过考虑用户相似性的更多因素,可以为目标用户选择更恰当的邻域用户,以更有效地提升推荐性能。在两个真实数据集上进行的实验表明,所提方法优于其他当前最好的相似度方法。
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