中国民航大学学报 ›› 2021, Vol. 39 ›› Issue (5): 40-43.

• 民用航空 • 上一篇    下一篇

基于嵌入表示的改进协同过滤旅游线路推荐

王洪建   

  1. (厦门航空有限公司,福建 厦门 361006)
  • 收稿日期:2021-04-19 修回日期:2021-04-19 接受日期:2021-01-21 出版日期:2021-10-20 发布日期:2021-10-31
  • 作者简介: 王洪建(1966—),男,山东龙口人,高级工程师,硕士,研究方向为计算机应用、智能优化 。

Tourism route recommendation based on latent representation and improved collaborative filtering

WANG Hongjian   

  1. Xiamen Airlines CO., LTD, Xiamen 361006, China
  • Received:2021-04-19 Revised:2021-04-19 Accepted:2021-01-21 Online:2021-10-20 Published:2021-10-31

摘要:

由于旅游数据集具有隐式反馈和极度稀疏性特点,限制了已有旅游线路推荐算法的性能。为解决上述问题,提出基于嵌入表示的改进协同过滤旅游线路推荐算法。首先,利用词向量模型将每条旅游线路表示成低维向量,并根据游客参与过的线路得到游客兴趣的向量表示;其次,根据旅游线路间的相似性得到游客的共现线路集合,并根据其相似性利用改进协同过滤算法完成线路推荐; 最后,经某真实旅游数据集验证,该算法可明显提高旅游线路推荐算法的性能。

关键词: 词向量, 改进协同过滤, 相似性, 稀疏性

Abstract: The existing tourism recommendation methods are limited because of the implicit feedback and extreme sparsity of tourism data sets. To solve the problem, an improved collaborative filtering algorithm based on embedding is proposed. Firstly, every route is represented as a low dimensional vector by using Doc2vector and every tourist is represented as a low dimensional vector based on all routes he/she had taken. Secondly, the co -occurrence routes between two tourists are obtained on the similarity of routes. Then the similarities among tourists are calculated according to the set of co-occurrence routes among them. Then, the routes are recommended by using the improved collaborative filtering. Finally, the method is proved to be effective on a real tourism data set.

Key words: word vector, improved collaborative filtering, similarity, data sparsity

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