大数据分析的智慧景点智能推荐模型  

Smart scenic spot intelligent recommendation model based on big data analysis

作　　者：徐丹[1] 张辉[2] XU Dan;ZHANG Hui(School of Liberal Arts,Shaanxi Institute of International Trade&Commerce,Xi’an 712046,China;China Northwest Architecture Design and Research Institute Co.,Ltd.,Xi’an 710000,China)

机构地区：[1]陕西国际商贸学院通识学院,西安712046 [2]中国建筑西北设计研究院有限公司,西安710000

出　　处：《信息技术》2023年第1期31-36,共6页Information Technology

基　　金：2020年陕西省教育厅科研项目(20JK0056)。

摘　　要：为了保证游客对推荐景点的满意度,以西安寺院为例,提出了大数据分析下的智慧景点智能推荐模型。采用Petri网景点游客分流模型获取超载景点以及可分流的目标景点集合,得出符合规则的目标分流游客信息集合后,通过协同过滤算法和情景上下文信息的结合,挖掘该信息集合内游客基本属性相似度、外部环境相似度以及综合相似度,依据评分值形成景点推荐集,完成智慧景点智能推荐。测试结果表明:该模型可有效完成游客负载分流,折扣累积利润和排序偏差准确率值均在0.9以上,实现游客数量最大化接纳,提升景点管理水平。In order to ensure tourists’ satisfaction with recommended scenic spots, taking Xi’an temple as an example, an intelligent scenic spot recommendation model based on big data analysis is proposed. The Petri net tourist diversion model is used to obtain the overloaded scenic spots and the target scenic spots that can be diverted. After obtaining the target diversion tourist information set that meets the rules, the basic attribute similarity of tourists in the information set, the similarity in the external environment and the comprehensive similarity are mined through the combination of collaborative filtering algorithm and scenario context information. Besides, the scenic spot recommendation set is formed according to the score value to complete the intelligent recommendation of smart scenic spots. The test results show that the model can effectively complete the load diversion of tourists, and the accuracy values of discount accumulative profit and ranking deviation are more than 0.9, so as to maximize the number of tourists and improve the management level of scenic spots.

关 键 词：大数据分析 智慧景点 分流模型 协同过滤算法 属性相似度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]