混合分层抽样统计与贝叶斯个性化排序的旅游景点推荐模型研究
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摘要
传统协同过滤推荐模型仅处理稀疏的评分数据,未深入挖掘用户及对象的潜在语义,且用户喜好信息也未充分利用.围绕旅游景点推荐这一热点问题,提出全新的混合分层抽样统计与贝叶斯个性化排序的推荐模型:采用分层抽样统计及主观赋值评价法刻画用户旅游喜好;基于矩阵分解算法(Matrix Factorization,简称MF)分析用户及对象(景点)的潜在语义,运用贝叶斯个性化排序算法(Bayesian Personalized Ranking,简称BPR)对推荐模型进行优化;综合用户旅游喜好信息及BPR优化结果,生成混合推荐列表.在新的"Wisdom Tourism"数据集上进行仿真实验.实验表明:推荐模型的RMSE、MAE、F1值较最强基线分别提升16.59%、10.05%、5.04%;相比于分层抽样统计方法,BPR算法在推荐过程中发挥更显著的作用.
Traditional recommendation system based on collaborative filtering only processes the sparse rating matrix.It doesn't extract the deep-level semantics of users(or items)as well as the users'preferences.To alleviate the above issues,a novel recommendation system for tourist spots based on Hierarchical Sampling Statistics(HSS)and Bayesian Personalized Ranking(BPR)is proposed.Users'preferences are generated and described firstly by the HSS algorithm and a subjective evaluation method.Then,deep-level semantics of users(or items)are extracted fully by the Matrix Factorization(MF)algorithm.And the state-of-art BPR algorithm is utilized in turn to optimize the entire recommendation model.Based on the users'preferences and the optimization results of the BPR algorithm,agroup of hybrid recommendation results are acquired and supplied to users.We demonstrate the effectiveness of our proposed model via extensive experiments on a novel"smart-travel"dataset created by ourselves.Experimental results show opposed to the best competitor,the RMSE,MAE and F1 value of the presented model improves about 16.59%,10.05% and 5.04%respectively.Compared against the HSS algorithm,the BPR algorithm has a more prominent role in the recommendation procedure.
Traditional recommendation system based on collaborative filtering only processes the sparse rating matrix.It doesn't extract the deep-level semantics of users(or items)as well as the users'preferences.To alleviate the above issues,a novel recommendation system for tourist spots based on Hierarchical Sampling Statistics(HSS)and Bayesian Personalized Ranking(BPR)is proposed.Users'preferences are generated and described firstly by the HSS algorithm and a subjective evaluation method.Then,deep-level semantics of users(or items)are extracted fully by the Matrix Factorization(MF)algorithm.And the state-of-art BPR algorithm is utilized in turn to optimize the entire recommendation model.Based on the users'preferences and the optimization results of the BPR algorithm,agroup of hybrid recommendation results are acquired and supplied to users.We demonstrate the effectiveness of our proposed model via extensive experiments on a novel"smart-travel"dataset created by ourselves.Experimental results show opposed to the best competitor,the RMSE,MAE and F1 value of the presented model improves about 16.59%,10.05% and 5.04%respectively.Compared against the HSS algorithm,the BPR algorithm has a more prominent role in the recommendation procedure.
引文
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[2]RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of net news[C]//Proceedings of ACM Conference on Computer Supported Cooperative Work,New York:ACM Press,1994:175-186.
[3]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.
[4]MILLER B N,RIEDL J T,KONSTAN J A.Experiences with groupLens:making usenet useful again[C]//Proceedings of the USENIX Winter Technical Conference,Anaheim:USENIX Press,1997:219-231.
[5]DALVI ESFAHANI M.A multi-criteria collaborative filtering recommender system using clustering and regression techniques[J].Social Science Electronic Publishing,2016,36(4):24-30.
[6]CHU W T,TSAI Y L.A hybrid recommendation system considering visual information for predicting favorite restaurants[J].World Wide Web-internet&Web Information Systems,2017,20(6):1313-1331.
[7]COVINGTON P,ADAMS J,SARGIN E.Deep neural networks for YouTube recommendation[C]//Proceedings of ACM Conference on Recommender System,New York:ACM Press,2016:191-198.
[8]HUANG P S,HE X D,GAO J F,et al.Learning deep structured semantic models for web search using click through data[C]//Proceedings of ACM International Conference on Information and Knowledge Management,Beijing:ACM Press,2013:2333-2338.
[9]HE R,MCCAULEY J.VBPR:visual Bayesian personalized ranking from implicit feedback[C]//Proceedings of AAAI,Phoenix:AAAI Press,2016:144-150.
[10]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of Conference on Uncertainty in Artificial Intelligence,USA:AUAI Press,2009:452-461.
[11]李涛.数据挖掘的应用与实践[M].厦门:厦门大学出版社,2013:123-158.LI T.Application and Practice of Data Mining[M].Xiamen:Xiamen University Press,2013:123-158.(Ch).
[12]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 C F.Improvement of Project-based Collaborative Filtering Algorithm[D].Nanchang:Jiangxi Normal University,2015.(Ch).
[13]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 C,ZHU Z G,ZHANG Y X,et al.Recommendation efficiency and personalization improvement based on user-based collaborative filtering algorithm[J].Small Microcomputer System,2016,37(3):428-432.(Ch).
[14]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 X L,FU Y Z,CHU P X.Application of Jake's similarity coefficient in recommendation model[J].Computer Technology and Development,2015,25(4):158-161+165.(Ch).
[15]BUHLMANN P,RUTIMANN P,GEER S,et al.Correlated variables in regression:clustering and sparse estimation[J].Journal of Statistical Planning&Inference,2013,143(11):1835-1858.
[16]王明佳,韩景倜,韩松乔.基于模糊聚类的协同过滤算法[J].计算机工程,2012,38(24):50-52.WANG M J,HAN J T,HAN S Q.Collaborative filtering algorithm based on fuzzy clustering[J].Computer Engineering,2012,38(24):50-52.(Ch).
[17]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Proceedings of International Conference on Learning Representation,San Diego:ICLR Press,2015.
[18]GONG Y,ZHANG Q.Hastage recommendation using attention-based convolutional neural network[C]//Proceedings of International Joint Conference on Artificial Intelligence,Phoenix:AAAI Press,2016:2782-2788.