基于轨迹挖掘模型的旅游景点推荐
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摘要
针对旅游推荐系统中基于内容的推荐和基于协同过滤的推荐方法的数据稀疏性和冷启动问题,以及现有轨迹挖掘方法忽略旅游轨迹中高级语义的问题,提出基于门控循环单元轨迹挖掘模型的推荐方法.为了充分利用旅游轨迹的高级语义信息,基于循环神经网络设计轨迹挖掘表示模型,对游客的旅游轨迹进行建模,在利用游客历史轨迹建模后向游客提供个性化旅游景点推荐.在真实旅游轨迹数据集上的实验表明,相比广泛使用的基线方法,文中方法在景点推荐的准确性和质量上都有一定提高.
A recommendation method based on the gated recurrent unit trajectory mining representation model(GRU-TMRM) is proposed to solve the problems of data sparsity and cold start in content based and collaborate filter based recommendation method, as well as the problem of ignoring rich semantics of travel track in track mining method. To take full advantage of semantics information contained in travel track, GRU-TMRM is designed. With GRU-TMRM, historical tracks of visitors can be modeled for providing personalized attractions recommendation. Experiments on real travel track dataset show that the proposed method effectively improves the accuracy and quality of recommendation compared with the widely used baseline method.
A recommendation method based on the gated recurrent unit trajectory mining representation model(GRU-TMRM) is proposed to solve the problems of data sparsity and cold start in content based and collaborate filter based recommendation method, as well as the problem of ignoring rich semantics of travel track in track mining method. To take full advantage of semantics information contained in travel track, GRU-TMRM is designed. With GRU-TMRM, historical tracks of visitors can be modeled for providing personalized attractions recommendation. Experiments on real travel track dataset show that the proposed method effectively improves the accuracy and quality of recommendation compared with the widely used baseline method.
引文
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[2] 孙彦鹏,古天龙,宾辰忠,等.基于多重隐语义表示模型的旅游路线挖掘.模式识别与人工智能,2018,31(5):462-469.(SUN Y P,GU T L,BIN C Z,et al.Travel Routing Mining Based on Multiple Latent Semantic Representation Model.Pattern Recognition and Artificial Intelligence,2018,31(5):462-469.)
[3] HSU F M,LIN Y T,HO T K.Design and Implementation of an Intelligent Recommendation System for Tourist Attractions:The Integration of EBM Model,Bayesian Network and Google Maps.Expert Systems with Applications,2012,39(3):3257-3264.
[4] FENZA G,FISCHETTI E,FURNO D,et al.A Hybrid Context Aware System for Tourist Guidance Based on Collaborative Filtering // Proc of the IEEE International Conference on Fuzzy Systems.Washington,USA:IEEE,2011:131-138.
[5] MORENO A,VALLS A,ISERN D,et al.SigTur/E-Destination:Ontology-Based Personalized Recommendation of Tourism and Lei-sure Activities.Engineering Applications of Artificial Intelligence,2013,26(1):633-651.
[6] LU E H C,FANG S H,TSENG V S.Integrating Tourist Packages and Tourist Attractions for Personalized Trip Planning Based on Travel Constraints.GeoInformatica,2016,20(4):741-763.
[7] 付永平,邱玉辉.一种基于贝叶斯网络的个性化协同过滤推荐方法研究.计算机科学,2016,43(9):266-268.(FU Y P,QIU Y H.Method of Personalized Collaboration Filter Recommendation Based on Bayesian Network.Computer Science,2016,43(9):266-268.)
[8] JIANG S H,QIAN X M,SHEN J J,et al.Author Topic Model-Based Collaborative Filtering for Personalized POI Recommendations.IEEE Transactions on Multimedia,2015,17(6):907-918.
[9] HUANG H S,GARTNER G.Using Context-Aware Collaborative Filtering for POI Recommendations in Mobile Guides // GARTNER G,ORTAG F,eds.Advances in Location-Based Services.Berlin,Germany:Springer,2012:131-147.
[10] MONREALE A,PINELLI F,TRASARTI R,et al.Wherenext:A Location Predictor on Trajectory Pattern Mining // Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2009:637-646.
[11] LE Q,MIKOLOV T.Distributed Representations of Sentences and Documents // Proc of the 31th International Conference on Machine Learning.New York,USA:ACM,2014:1188-1196.
[12] RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing Personalized Markov Chains for Next-Basket Recommendation // Proc of the 19th International Conference on World Wide Web.New York,USA:ACM,2010:811-820.
[13] HE R N,MCAULEY J.Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation // Proc of the 16th IEEE International Conference on Data Mining.Washington,USA:IEEE,2016:191-200.
[14] BOGINA V,KUFLIK T.Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks[C/OL].[2018-06-25].http://120.52.51.14/ceur-ws.org/Vol-1922/paper11.pdf.
[15] HIDASI B,QUADRANA M,KARATZOGLOU A,et al.Parallel Recurrent Neural Network Architectures for Feature-Rich Session-Based Recommendations // Proc of the 10th ACM Conference on Recommender Systems.New York,USA:ACM,2016:241-248.
[16] MOBASHER B,DAI H H,LUO T,et al.Using Sequential and Non-sequential Patterns in Predictive Web Usage Mining Tasks // Proc of the IEEE International Conference on Data Mining.Washing-ton,USA:IEEE,2002:669-672.
[17] BONNIN G,JANNACH D.Automated Generation of Music Playlists:Survey and Experiments.ACM Computing Surveys,2014,47(2).DOI:10.1145/2652481.
[18] HARIRI N,MOBASHER B,BURKE R.Context-Aware Music Re-commendation Based on Latenttopic Sequential Patterns // Proc of the 6th ACM Conference on Recommender Systems.New York,USA:ACM,2012:131-138.
[19] YAP G E,LI X L,YU P S.Effective Next-Items Recommendation via Personalized Sequential Pattern Mining // Proc of the 17th International Conference on Database Systems for Advanced Applications.Berlin,Germany:Springer,2012:48-64.
[20] MOLING O,BALITRUNAS L,RICCI F.Optimal Radio Channel Recommendations with Explicit and Implicit Feedback // Proc of the 6th ACM Conference on Recommender Systems.New York,USA:ACM,2012:75-82.
[21] SHANI G,HECKERMAN D,BRAFMAN R I,et al.An MDP-Based Recommender System.Journal of Machine Learning Research,2005,6:1265-1295.
[22] AGHDAM M H,HARIRI N,MOBASHER B,et al.Adapting Recommendations to Contextual Changes Using Hierarchical Hi-dden Markov Models // Proc of the 9th ACM Conference on Reco-mmender Systems.New York,USA:ACM,2015:241-244.
[23] DU N,DAI H J,TRIVEDI R,et al.Recurrent Marked Temporal Point Processes:Embedding Event History to Vector // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2016:1555-1564.
[24] LIU Y C,LIU C,LIU B,et al.Unified Point-of-Interest Reco-mmendation with Temporal Interval Assessment // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2016:1015-1024.
[25] DAVIDSON J,LIEBALD B,LIU J N,et al.The YouTube Video Recommendation System // Proc of the 4th ACM Conference on Recommender Systems.New York,USA:ACM,2010:293-296.
[26] LINDEN G,SMITH B,YORK J.Amazon.com Recommendations:Item-to-Item Collaborative Filtering.IEEE Internet Computing,2003,7(1):76-80.
[27] RENDLE S,FRAUDENTHALER C,GANTNER Z,et al.BPR:Bayesian Personalized Ranking from Implicit Feedback // Proc of the 25th Conference on Uncertainty in Artificial Intelligence.Arlington,USA:AUAI Press,2009:452-461.