ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (2): 405-414.doi: 10.7544/issn1000-1239.2017.20150822

• 信息处理 • 上一篇    下一篇

GeoPMF:距离敏感的旅游推荐模型

张伟,韩林玉,张佃磊,任鹏杰,马军,陈竹敏   

  1. (山东大学计算机科学与技术学院 济南 250101) (will_zhang2014@outlook.com)
  • 出版日期: 2017-02-01
  • 基金资助: 
    国家自然科学基金项目(61272240,61672322);山东省自然科学基金项目(ZR2012FM037);微软国际合作基金项目(FY14-RES-THEME-25)

GeoPMF: A Distance-Aware Tour Recommendation Model

Zhang Wei, Han Linyu, Zhang Dianlei, Ren Pengjie, Ma Jun, Chen Zhumin   

  1. (School of Computer Science and Technology, Shandong University, Jinan 250101)
  • Online: 2017-02-01

摘要: 虽然目前旅游者可以利用Web搜索引擎来选择旅游景点,但往往难以获得较好符合自身需要的旅游规划.而旅游推荐系统是解决上述问题的有效方式.一个好的旅游推荐模型应具有个性化并能考虑用户时间和费用的限制.调研表明,用户在选择旅游景点时,目的地与用户常居地的距离常常是一个需要考虑的问题.因为旅行距离往往可以间接地反映了时间和费用的影响.于是,在贝叶斯模型和概率矩阵分解模型的基础上,提出一个旅行距离敏感的旅游推荐模型(geographical probabilistic matrix factorization, GeoPMF).主要思想是基于每个用户的旅游历史,推算出一个最偏好的旅游距离,并作为一种权重,添加到传统的基于概率矩阵分解的推荐模型中.在携程网站的旅游数据集上的实验表明,与基准方法相比,GeoPMF 的RMSE(root mean square error)可以降低近10%;与传统概率矩阵分解模型(PMF)相比,通过考虑距离因子,RMSE平均降幅近3.5%.

关键词: 旅游推荐, 推荐系统, 概率矩阵分解模型, 距离敏感, GeoPMF算法

Abstract: Although people can use Web search engines to explore scenic spots for traveling, they often find it very difficult to discover the sighting sites which match their personalized need well. Tour recommendation systems can be used to solve the issue. A good tour recommendation system should be able to provide personalized recommendation and take the time and cost factors into account. Furthermore, our investigation shows that often a user u will consider the distance between her/his habitual residence and the tour destination when she/he makes her/his travel plan. It is because that the travel distance reflects the effect of time and cost indirectly. Therefore, we propose a distance-aware tour recommendation model, named GeoPMF (geographical probabilistic matrix factorization), which is developed based on the Bayesian model and PMF (probabilistic matrix factorization). The main idea of GeoPMF is that for each user we try to get a most preferred travel distance span by mining her past tour records. Then we use it as a kind of weight factors added into the traditional PMF model. Experiments on travel data of Ctrip show that, our new method can decrease RMSE (root mean square error) nearly 10% compared with some baseline methods. And when compared with the traditional PMF model, the average decline on RMSE is nearly 3.5% in virtue of the distance factor.

Key words: tour recommendation, recommender system, probabilistic matrix factorization (PMF) model, distance-aware, GeoPMF

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