基于在线评论与深度学习的旅游目的地推荐模型  

A destination recommendation system based on online reviews and deep learning

作　　者：谭鸿博 苏甜[1] 张思盈 荣幸 孙伊琳 矫琪 林知浩 郑天翔 TAN Hongbo;SU Tian;ZHANG Siying;RONG Xing;SUN Yilin;JIAO Qi;LIN Zhihao;ZHENG Tianxiang(Shenzhen Campus,Jinan University,Shenzhen 518053,Guangdong,China)

机构地区：[1]暨南大学深圳校区,广东深圳518053

出　　处：《陕西师范大学学报(自然科学版)》2025年第2期101-113,共13页Journal of Shaanxi Normal University:Natural Science Edition

基　　金：暨南大学深圳校区学科创新发展“启航”基金项目(JNSZQH2302);国家级大学生创新创业训练计划项目(202410559009)。

摘　　要：近年来,旅游社交媒体平台上的在线评论数据被广泛应用于旅游大数据分析研究中,而在目的地推荐应用方面仍有待深入。使用Python编程语言,通过网络爬虫在携程旅行网上收集了239个中国5A级旅游景区的评论数据,并利用Embedding和BERT等自然语言处理和深度学习技术,构建了一个旅游目的地推荐模型;通过收集到的57360条评论数据对模型进行训练和验证,最终在14340条测试数据上得到78%左右的正确率。实验结果表明,借助其他旅游者的切身经历和对旅游目的地的形象感知,可以提高潜在旅游者找到理想目的地的效率,有助于旅游者规划旅游行程的第一步。研究成果扩充了在线评论数据的研究范畴,同时为旅游者在旅游咨询问题上提供了新的解决思路和技术支撑。Though online reviews on social media platforms have been widely used in tourism research as data analytical sources in recent years,how they can be applied to destination recommendation needs further investigation.The review data of 2395A scenic spots in China was retrieved from ctrip.com by Python programming and web-crawling technology.Natural language processing and deep learning technologies including BERT(Bidirectional Encoder Representations from Transformers)and word embedding were then imported to build a destination recommendation system for tourist destinations.The model was trained and validated on a dataset containing 57360 reviews,with a classification accuracy of around 78%reached on 14340 pieces of test data.Experimental results show that,with the aid of other tourists travel experiences and image perception,the proposed model can facilitate potential tourists in finding their ideal destinations to explore the first step of itinerary planning.The findings of this study extend the research scope of online reviews within tourism and hospitality and provide new insights into pre-trip travel counseling.

关 键 词：目的地推荐 行程规划 在线评论 深度学习 预测模型 

分 类 号：F592[经济管理—旅游管理] TP183[经济管理—产业经济]