计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 226-230.doi: 10.11896/jsjkx.200900119
孙振强, 罗永龙, 郑孝遥, 章海燕
SUN Zhen-qiang, LUO Yong-long, ZHENG Xiao-yao, ZHANG Hai-yan
摘要: 近年来,随着社交网络的发展,如何设计出符合用户个性化需求的路径推荐方法,已成为重要的研究热点。文中考虑了兴趣点的相关特征,将用户的情感与商品相似度融入蚁群算法的启发函数中,采用精英管理蚂蚁策略,最大化管理蚂蚁策略的改进策略,同时利用粒子群算法改进蚁群算法的初次信息素分布,结合数据集中593名游客的评分以及文本评论数据,提出基于粒子群-蚁群的旅游路径推荐算法(Particle Swarm-Ant Colony algorithm for user emotion and similarity,PS-AC)算法,并用改进后的蚁群算法实现环游景区内高人气景点的用户旅游路径推荐。基于真实的数据集测试表明,所提算法在精确率、召回率、F测度值上均有较好的性能。
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