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。
In recent years,with the development of social networks,how to design a path recommendation method that meets the individual needs of users has become an important research hotspot.This paper considers the relevant characteristics of POI(point of interests),integrates the user’s emotion and product similarity into the heuristic function of the ant colony algorithm,adopts the improvement strategy of EMAS,MMAS.By using the particle swarm algorithm to improve the first pheromone distribution of ant colony algorithm,combined with the scores of 593 tourists in the dataset and text comment data,this paper proposes the PS-AC(Particle Swarm-Ant Colony algorithm for user emotion and similarity)algorithm,and uses the improved ant colony algorithm to realize the user’s travel route recommendation of the highly popular scenic spots in the scenic spots.Tests on real data sets show that the PS-AC algorithm has good performance in accuracy,recall,and F measurement.
作者
孙振强
罗永龙
郑孝遥
章海燕
SUN Zhen-qiang;LUO Yong-long;ZHENG Xiao-yao;ZHANG Hai-yan(School of ComputerandInformation,Anhui Normal University,Wuhu,Anhui 241000,China)
出处
《计算机科学》
CSCD
北大核心
2021年第S01期226-230,共5页
Computer Science
基金
国家自然科学基金面上项目(61972439)。
关键词
蚁群算法
旅游路线推荐
个性化推荐
粒子群算法
用户相似度
Ant colony algorithm
Travel route recommendation
Personalized recommendation
Particle swarm optimization
User similarity
作者简介
通信作者:孙振强,born in 1996,postgraduate.His main research interests include intelligent computing and recom-mendation system.2716214298@qq.com。
