计算机科学 ›› 2016, Vol. 43 ›› Issue (9): 227-231.doi: 10.11896/j.issn.1002-137X.2016.09.045
高山,刘炜,崔勇,张茜,王宗敏
GAO Shan, LIU Wei, CUI Yong, ZHANG Qian and WANG Zong-min
摘要： 协同过滤作为目前应用最成功的个性化推荐技术,在电子商务、社交网络等领域得到了广泛应用。然而,当此类算法应用到个性化医疗推荐领域时,由于个人医疗行为本身的复杂性和多样性,出现了推荐准确率下降的问题。针对这一问题,提出一种融合多种用户行为的协同过滤推荐算法,使用权重因子来综合衡量不同用户行为对推荐质量的影响,并引入重合依赖度的概念来修正传统的相似度度量方法。在收集的Top-md数据集上的实验结果表明,该算法能够全方位表达用户的就医偏好和意愿,有效提高个性化医疗推荐系统的推荐质量。
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