混合分层抽样与协同过滤的旅游景点推荐模型研究
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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.
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.
引文
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[2]Powell M J D.Approximation theory and methods[M].Cambridge:Cambridge University Press,1981:35-40.
[3]Salton G.Automatic text processing[J].Science,1970,168(3929):335-343.
[4]Goldberg D,Nichols D,Oki B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,2013,35(12):61-70.
[5]Resnick P,Iacovou N,Suchak M,et al.GroupLens:An open architecture for collaborative filtering of netnews[C]//1994ACM Conference on Computer Supported Cooperative Work.Chapel Hill,North Carolina:ACM,1994:175-186.
[6]Konstan J A,Miller B N,Maltz D,et al.GroupLens:Applying collaborative filtering to usenet news[J].Communications of the ACM,1997,40(3):77-87.
[7]Miller B N,Riedl J T,Konstan J A.Experiences with grouplens:Making usenet useful again[C]//Annual Conference on Usenix Winter Technical Conference.Anaheim,California:ACM,1997:219-231.
[8]Kim K,Ahn H.Recommender systems using cluster-indexing collaborative filtering and social data analytics[J].International Journal of Production Research,2017,55(17):5037-5049.
[9]DMehrbakhsh Nilashi,Mohammad Dalvi Esfahani,Morteza Zamani Roudbaraki,et al.A multi-criteria collaborative filtering recommender system using clustering and regression techniques[J].Social Science Electronic Publishing,2016,3(5):24-30.
[10]Hill W,Stead L,Rosenstein M,et al.Recommending and evaluating choices in a virtual community of use[C]//The SIGCHIConference on Human Factors in Computing Systems.Denver,Colorado:ACM,1995:194-201.
[11]Shardanand U,Maes P.Social information filtering:Algorithms for automating"word of mouth"[C]//The SIGCHI Conference on Human Factors in Computing Systems.Denver,Colorado:ACM,1995:210-217.
[12]长沙冉星信息科技有限公司.问卷调查.[EB/OL](2018-01-02).https://www.wjx.cn/[OL].
[13]李涛,王建东,叶飞跃,等.一种基于用户聚类的协同过滤推荐算法[J].系统工程与电子技术,2007,29(7):1178-1182.Li Tao,Wang Jiandong,Ye Feiyue,et al.Collaborative filtering recommendation algorithm based on clustering basal users[J].Systems Engineering and Electronics,2007,29(7):1178-1182.
[14]黄传飞.基于项目的协同过滤算法的改进[D].南昌:江西师范大学,2015.Huang Chuanfei.An improved item based collaborative filtering algorithm[D].Nanchang:Jiangxi Normal University,2015.
[15]王成,朱志刚,张玉侠,等.基于用户的协同过滤算法的推荐效率和个性化改进[J].小型微型计算机系统,2016,37(3):428-432.Wang Cheng,Zhu Zhigang,Zhang Yuxia,et al.Improvement in recommendation efficiency and personalized of user-based collaborative filtering algorithm[J].Journal of Chinese Computer System,2016,37(3):428-432.
[16]张晓琳,付英姿,褚培肖.杰卡德相似系数在推荐模型中的应用[J].计算机技术与发展,2015,25(4):158-161,165.Zhang Xiaolin,Fu Yingzi,Chu Peixiao.Application of Jaccard similarity coefficient in recommender system[J].Computer Technology and Development,2015,25(4):158-161,165.
[17]俞立平,潘云涛,武夷山.科技教育评价中主客观赋权方法比较研究[J].科研管理,2009,30(4):154-161.Yu Liping,Pan Yuntao,Wu Yishan.Comparing objective weighting with subjective weighting in sci-tech education institute assessment[J].Science Research Management,2009,30(4):154-161.
[18]Jayaraman D,Sha F,Grauman K.Decorrelating semantic visual attributes by resisting the urge to share[C]//IEEE Conference on Computer Vision and Pattern Recognition.Columbus,Ohio:IEEE,2014:1629-1936.
[19]He K,Zhang X,Ren S,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Columbus,Ohio:IEEE,2016:770-778.