基于知识图谱和频繁序列挖掘的旅游路线推荐
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摘要
大数据在提供海量多源信息的同时,也带来了信息过载问题,这在旅游领域内表现得尤为突出。针对当前游客在制定旅行路线时需要花费大量时间和精力的现状,首先,提出一种融合多源旅游数据构建知识图谱的方法,有效地抽取相关旅游领域知识;其次,利用知识图谱及大量旅行游记生成旅游路线数据库,并提出一种能够根据游客类型生成海量候选路线的频繁路线序列模式挖掘算法;最后,设计了一种多维度路线搜索和排序机制来为用户推荐个性化的旅游路线。基于真实旅游大数据的实验结果表明,该方法可以同时考虑旅行天数、人物类型和景点类型喜好等多方面因素,帮助游客快速制定个性化的旅行路线,有效提升游览体验。
Big data provide a huge amount of information and bring the information overload problem to users.This situation is more serious in tourism field.To solve the problem that tourists need to spend a lot of time and energy to prepare a travel plan,this paper firstly proposed a method to construct a knowledge graph incorporating heterogeneous tourism data to extract the knowledge in tourism field.Secondly,this paper used the knowledge graph and travelogues to generate the travel route database,and proposed a frequent route sequence pattern mining algorithm which can generate mass candidate routes according to the tourists' type.Finally,a multi-dimensional route searching and ranking mechanism was designed to recommend personalization travel routes.The experimental results illustrate that the proposed method can adopt various factors,i.e.the number of tourist's travel days,user types and attractions preferences,to help tourists to design personalized travel routes and effectively improve the tour experience.
Big data provide a huge amount of information and bring the information overload problem to users.This situation is more serious in tourism field.To solve the problem that tourists need to spend a lot of time and energy to prepare a travel plan,this paper firstly proposed a method to construct a knowledge graph incorporating heterogeneous tourism data to extract the knowledge in tourism field.Secondly,this paper used the knowledge graph and travelogues to generate the travel route database,and proposed a frequent route sequence pattern mining algorithm which can generate mass candidate routes according to the tourists' type.Finally,a multi-dimensional route searching and ranking mechanism was designed to recommend personalization travel routes.The experimental results illustrate that the proposed method can adopt various factors,i.e.the number of tourist's travel days,user types and attractions preferences,to help tourists to design personalized travel routes and effectively improve the tour experience.
引文
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[13]JIANG S,QIAN X,MEI T,et al.Personalized Travel Se-quence Recommendation on Multi-Source Big Social Media[J].IEEE Transactions on Big Data,2016,2(1):43-56.
[14]CHANG L,CAO Y T,SUN W P,et al.Review on tourism recommendation system[J].Computer Science,2017,44(10):1-6.(in Chinese)常亮,曹玉婷,孙文平,等.旅游推荐系统研究综述[J].计算机科学,2017,44(10):1-6.
[15]QI G L,GAO H,WU T X.The research advances of knowledge graph[J].Technology Intelligence Engineering,2017,3(1):4-25.(in Chinese)漆桂林,高桓,吴天星.知识图谱研究进展[J].情报工程,2017,3(1):4-25.
[16]LU C,LAUBLET P,STANKOVIC M.Travel Attractions Recommendation with Knowledge Graphs[M]∥Knowledge Engineering and Knowledge Management.Springer Internatio-nal Publishing,2016.
[17]TSAI C Y,LAI B H.A Location-Item-Time sequential pattern mining algorithm for route recommendation[J].KnowledgeBased Systems,2015,73:97-110.
1)https://query.wikidata.org/
2 )http://lbs.amap.com
[2] MURPHY H C,CHEN M M,COSSUTTA M.An investigation of multiple devices and information sources used in the hotel booking process[J].Tourism Management,2016,52:44-51.
[3] BANERJEE S,CHUA A Y K.In search of patterns among travellers’hotel ratings in TripAdvisor[J].Tourism Man-agement,2016,53:125-131.
[4] GUO L M,GAO X,WU B,et al.Discovering common behavior using staying duration on semantic trajectory[J].Journal of Computer Research and Development,2017,54(1):111-122.(in Chinese)郭黎敏,高需,武斌,等.基于停留时间的语义行为模式挖掘[J].计算机研究与发展,2017,54(1):111-122.
[5] GAO Q,ZHANG F L,WANG R J,et al.Trajectory big data:a review of key technologies in data processing[J].Journal of Software,2017,28(4):959-992.(in Chinese)高强,张凤荔,王瑞锦,等.轨迹大数据:数据处理关键技术研究综述[J].软件学报,2017,28(4):959-992.
[6] SUN Y,FAN H,BAKILLAH M,et al.Road-based travel recommendation using geo-tagged images[J].Computers Environment&Urban Systems,2015,53:110-122.
[7] WEI L Y,ZHENG Y,PENG W C.Constructing popular routes from uncertain trajectories[C]∥Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2012:195-203.
[8] RAHIMI S M,WANG X.Location Recommendation Based on Periodicity of Human Activities and Location Categories[M]∥Advances in Knowledge Discovery and Data Mining.Springer Berlin Heidelberg,2013:377-389.
[9] TONG G,GUO B,YI O,et al.Crowd Travel:scenic spot profiling by using heterogeneous crowdsourced data[J].Journal of Ambient Intelligence&Humanized Computing,2017(5):1-10.
[10]ARAIN Q A,MEMON H,MEMON I,et al.Intelligent travel information platform based on location base services to predict user travel behavior from user-generated GPS traces[J].International Journal of Computers&Applica-tions,2017(3):155-168.
[11]CHEN X,ZHANG Y,MA P,et al.A Package Generation and Recommendation Framework Based on Travelogues[C]∥IEEE,Computer Software and Applications Conference.IEEE Computer Society,2015:692-701.
[12]ZHU Z,SHOU L,CHEN K.Get into the spirit of a location by mining user-generated travelogues[J].Neurocomputing,2016,204:61-69.
[13]JIANG S,QIAN X,MEI T,et al.Personalized Travel Se-quence Recommendation on Multi-Source Big Social Media[J].IEEE Transactions on Big Data,2016,2(1):43-56.
[14]CHANG L,CAO Y T,SUN W P,et al.Review on tourism recommendation system[J].Computer Science,2017,44(10):1-6.(in Chinese)常亮,曹玉婷,孙文平,等.旅游推荐系统研究综述[J].计算机科学,2017,44(10):1-6.
[15]QI G L,GAO H,WU T X.The research advances of knowledge graph[J].Technology Intelligence Engineering,2017,3(1):4-25.(in Chinese)漆桂林,高桓,吴天星.知识图谱研究进展[J].情报工程,2017,3(1):4-25.
[16]LU C,LAUBLET P,STANKOVIC M.Travel Attractions Recommendation with Knowledge Graphs[M]∥Knowledge Engineering and Knowledge Management.Springer Internatio-nal Publishing,2016.
[17]TSAI C Y,LAI B H.A Location-Item-Time sequential pattern mining algorithm for route recommendation[J].KnowledgeBased Systems,2015,73:97-110.
1)https://query.wikidata.org/
2 )http://lbs.amap.com