计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 56-61.doi: 10.11896/j.issn.1002-137X.2019.02.009

• 大数据与数据科学 • 上一篇    下一篇

基于知识图谱和频繁序列挖掘的旅游路线推荐

孙文平, 常亮, 宾辰忠, 古天龙, 孙彦鹏   

  1. 桂林电子科技大学广西可信软件重点实验室 广西 桂林541004
  • 收稿日期:2018-01-26 出版日期:2019-02-25 发布日期:2019-02-25
  • 通讯作者: 宾辰忠(1979-),男,博士生,讲师,主要研究方向为位置服务、智能推荐及数据挖掘,E-mail:binchenzhong@guet.edu.cn
  • 作者简介:孙文平(1990-),男,硕士生,主要研究方向为机器学习与智能规划;常 亮(1980-),男,博士,教授,CCF高级会员,主要研究方向为知识工程、智能规划、形式化方法;古天龙(1964-),男,博士,教授,主要研究方向为形式化方法、符号计算等;孙彦鹏(1993-),男,硕士生,主要研究方向为机器学习、数据挖掘与推荐系统。
  • 基金资助:
    本文受国家自然科学基金项目(U1501252,61572146),广西创新驱动重大专项项目(AA17202024),广西自然科学基金项目(2016GXNSFDA380006),广西信息科学实验中心平台建设项目(PT1601),广西可信软件重点实验资助课题(KX201729)资助。

Travel Route Recommendation Based on Knowledge Graph and Frequent Sequence Mining

SUN Wen-ping, CHANG Liang, BIN Chen-zhong, GU Tian-long, SUN Yan-peng   

  1. Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2018-01-26 Online:2019-02-25 Published:2019-02-25

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。

关键词: 旅游路线推荐, 频繁序列模式挖掘, 用户生成内容, 知识图谱

Abstract: Big data provide a huge amount of information and bring the information overload problem to users.This situa-tion 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 gene-rate 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.

Key words: Frequent sequence pattern mining, Knowledge graph, Travel route recommendation, User generated data

中图分类号: 

  • TP118
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