计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 238-241.doi: 10.11896/jsjkx.210400088
蔡晓娟1, 谭文安1,2
CAI Xiao-juan1, TAN Wen-an1,2
摘要： 电子商务的迅猛发展在给用户提供更多商务选择的同时也导致了信息的泛滥。推荐系统作为信息过滤技术中必不可少的一种方法获得了社会的普遍关注。协同过滤算法是推荐系统中应用最广泛的技术,但其面临数据稀疏性、冷启动、数据扩展性等问题。文中提出了一种改进的融合相似度和信任度的协同过滤算法,该算法包括3个步骤:首先,计算用户之间的信任度;其次,计算用户之间的相似度;最后,融合信任度和相似度以计算用户之间的信任值,从而得到最终的评分预测方程。实验结果表明,针对不同的邻域集,所提算法的性能均优于传统协同过滤算法。
中图分类号:
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