Travel Attractions Recommendation Based on Trajectory Mining Representation Model
ZHANG Shunyao1, CHANG Liang2, GU Tianlong1, BIN Chenzhong2, SUN Yanpeng3, ZHU Guiming1, JIA Zhonghao1
1.School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004; 2.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004; 3.School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004
Abstract 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.
Fund:Supported by National Natural Science Foundation of China(No.U1501252,61572146), Natural Science Foundation of Guangxi Province(No.2016GXNSFDA380006), Innovation-Driven Major Projects of Guangxi Province(No.AA17202024), Guangxi Information Science Experiment Center Platform Construction Project(No.PT1601),Guangxi University Young and Middle-Aged Teachers Basic Ability Improvement Project(No.2018KYD203), Guangxi Trusted Software Key Experimental Funding Project(No.KX201729)
Corresponding Authors:
(BIN Chenzhong(Corresponding author), Ph.D. candidate, lecturer. His research interests include data mining and intelligent re-commendation.)
About author:: (ZHANG Shunyao, master student. His research interests include machine learning, knowledge graph and recommendation system.)(CHANG Liang, Ph.D., professor. His research interests include knowledge enginee-ring and symbolic reasoning.)(GU Tianlong, Ph.D., professor. His research interests include knowledge enginee-ring and symbolic reasoning.)(SUN Yanpeng, master student. His research interests include machine learning, data mining and recommendation system.)(ZHU Guiming, master student. His research interests include machine learning, recommendation system and knowledge graph.)(JIA Zhonghao, master student. His research interests include machine learning, recommendation system and knowledge graph.)
[1] 常 亮,曹玉婷,孙文平,等.旅游推荐系统研究综述.计算机科学, 2017, 44(10): 1-6. (CHANG L, CAO Y T, SUN W P, et al. Review on Tourism Reco-mmendation System. Computer Science, 2017, 44(10): 1-6.) [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.
2019, Vol. 32 
