›› 2016, Vol. 28 ›› Issue (6): 113-118.

• 市场营销 • 上一篇    下一篇

考虑用户口碑的旅游计划个性化推荐方法研究

李倩, 许伟, 蒋洪迅   

  1. 中国人民大学信息学院, 北京 100872
  • 收稿日期:2014-05-27 出版日期:2016-06-28 发布日期:2016-07-07
  • 通讯作者: 蒋洪迅(通讯作者),中国人民大学信息学院副教授,硕士生导师,博士。
  • 作者简介:李倩,中国人民大学信息学院讲师,硕士生导师,博士;许伟,中国人民大学信息学院副教授,博士生导师,博士。
  • 基金资助:

    国家自然科学基金项目(71301163;71571183);教育部人文社科基金项目(12YJA630046;14YJA630075)。

Personalized Recommendation for Traveling Planning Based on Online Word-of-mouth

Li Qian, Xu Wei, Jiang Hongxun   

  1. School of Information, Renmin University of China, Beijing 100872
  • Received:2014-05-27 Online:2016-06-28 Published:2016-07-07

摘要:

移动互联网的快速发展,为人们提供了更及时的资讯和服务。传统旅游网站的景点和旅游线路展示方式,对于移动终端来说内容过于丰富,不适合浏览和选择。本文针对移动互联网特点,提出了一种考虑用户口碑的旅游计划个性化推荐方法。该方法首先利用语义分析方法,研究用户需求和旅游计划之间的多层次匹配关系。其次,深入挖掘用户口碑,提出了一个基于用户口碑的动态调整方法。更进一步地,结合用户需求匹配和用户口碑,提出了集成的旅游路线个性化推荐方法。最后,基于提出的推荐方法,本研究构建了一个旅游推荐原型系统,并进行了方法评价。结果表明,本文提出的方法较传统方法有明显的改进,具有更好的用户体验,为用户旅游提供了便捷服务,具有较好的理论意义和实践价值。

关键词: 旅游计划, 智能推荐, 用户需求, 用户口碑, 移动互联网

Abstract:

With the rapid development of internet, it provides more information and services for users. Traditional methods for scenic spot and traveling routes are not fit for mobile network because of plenteous content. This paper proposes a personalized recommendation method for traveling planning based on online word-of-mouth. In the proposed method, a users' matching model is suggested for finding the similarities between users' demand and traveling planning. Second, a dynamic user's opinion-based web mining model is offered to mine online word-of-mouth. Finally, an integrated model is proposed for combining users' demand matching and opinions. To verify the efficiency of the proposed method, a demo for traveling recommendation is developed and a survey is carried out for method evaluation. The empirical results show that the performance of the proposed method outperforms ones of traditional methods, and the proposed method has better users' experiences. Our paper provides an alternative and efficient method for traveling recommendation.

Key words: traveling planning, intelligent recommendation, users'demand, users'opinion, mobile network