计算机科学 ›› 2017, Vol. 44 ›› Issue (2): 88-92.doi: 10.11896/j.issn.1002-137X.2017.02.011
• 2016 第十三届全国Web 信息系统及其应用学术会议 • 上一篇 下一篇
李改,陈强,李磊,潘进财
LI Gai, CHEN Qiang, LI Lei and PAN Jin-cai
摘要: 单类个性化协同排序算法的研究的核心思想是把单类协同过滤问题当成排序问题来看待。之前的研究仅仅使用了隐式反馈数据来对推荐对象进行排序,这限制了推荐的准确度。随着在线社交网络的出现,为了进一步提高单类个性化协同排序算法的准确度,提出了一种新的融合社交网络的单类个性化协同排序算法。在真实的包含社交网络的2个数据集上的实验验证了该算法在各个评价指标下的性能均优于几个经典的单类协同过滤算法。实验证明,社交网络信息对于提高单类个性化协同排序算法的性能具有重要作用。
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