摘要
采用问卷调查与自动抓取相结合的方式,采集用户信息、用户评分等旅游数据,对数据做分层抽样,生成包含用户旅游喜好信息的“智慧旅游”数据集。围绕该数据集,预处理用户评分并执行基于用户聚类的协同过滤算法,以计算目标用户与聚类中心的相似性。结合分层抽样模型生成的旅游喜好信息,输出混合推荐列表。实验结果表明:相比基线,混合分层抽样与协同过滤的推荐模型对评分预测的均方根误差(Root mean square error,RMSE)和平均绝对误差(Mean absolute error,MAE)分别降低11.5%~64.9%和18.8%~47.7%。混合推荐的准确率和召回率相比基线也有较大程度提升,旅游景点推荐效果良好。
By combining the method of questionnaire survey and automatic crawling,a lot of useful tourist information such as users’personal information,users’ratings of tourist attractions and other tourism data are obtained.Based on the crawled tourism data,a hierarchical sampling method is applied in turn to generate the“Smart Travel”dataset which contains the important demographic information.Then a user clustering-based collaborative filtering algorithm is implemented to compute the semantic similarity between target user and each clustering center after the users’ratings of tourist attractions in the“Smart Travel”dataset is preprocessed.Finally,a hybrid recommendation list is generated by absorbing the demographic information obtained by the hierarchical sampling model.Experimental results show that compared with the traditional method,two evaluating indicators like the root mean square error(RMSE)and the mean absolute error(MAE)of the presented algorithm reduce 11.5%-64.9%and 18.8%-47.7%,respectively.Meanwhile,compared with the main baselines,the recommendation precision gets a large improvements as well as the recall rate and better recommendation results are obtained ultimately.
作者
李广丽
朱涛
袁天
滑瑾
张红斌
Li Guangli;Zhu Tao;Yuan Tian;Hua Jin;Zhang Hongbin(School of Information Engineering,East China Jiaotong University,Nanchang,330013,China;Software School,East China Jiaotong University,Nanchang,330013,China;Computer School,Wuhan University,Wuhan,430072,China)
出处
《数据采集与处理》
CSCD
北大核心
2019年第3期566-576,共11页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61762038,61861016)资助项目
江西省科技厅自然科学基金(20171BAB202023)资助项目
江西省科技厅重点研发计划(20171BBG70093)资助项目
教育部人文社会科学研究规划基金(17YJAZH117,16YJAZH029)资助项目
江西省社会科学规划项目(16TQ02)资助项目
关键词
分层抽样
聚类
协同过滤
旅游景点
推荐模型
hierarchical sampling
clustering
collaborative filtering
tourist attractions
recommendation model