基于互联网信息的多约束多目标旅游路线推荐

J4 ›› 2016, Vol. 38 ›› Issue (01): 163-170.

基于互联网信息的多约束多目标旅游路线推荐

陆国锋1，黄晓燕2，吕绍和1，王晓东1

(1.国防科学技术大学并行与分布处理重点实验室，湖南长沙 410073;
2.78086部队，四川成都 610017)

收稿日期:2014-12-18 修回日期:2015-11-26 出版日期:2016-01-25 发布日期:2016-01-25
基金资助:
国家自然科学基金（61170260）

Multi-constraint and multi-objective trip
recommendation based on internet information

LU Guofeng1 ,HUANG Xiaoyan2 ,L Shaohe1 ,WANG Xiaodong1

(1.National Key Laboratory of Parallel and Distributed Processing,
National University of Defense Technology,Changsha 410073;
2.Troops 78086,Chengdu 610017,China)

Received:2014-12-18 Revised:2015-11-26 Online:2016-01-25 Published:2016-01-25

摘要/Abstract

摘要：

针对新游客在陌生城市如何规划旅游路线的问题，研究基于景点评分机制以及用户多约束的旅游路线推荐问题。首先提取景点的开放时间、门票与GPS坐标等及旅游网站上对于景点的评价信息等；然后提出一种基于多约束的k贪心算法，可以为游客推荐较好的旅游线路，并有效消除了推荐系统对先验知识的依赖。以驴评网上北京著名景点的信息作为数据集，实现并评估了推荐算法。实验结果表明，该方法能够为用户提供准确合理的路线规划。

关键词: 评分机制, 景点综合信息, 最优旅游线路

Abstract:

To help a novice visitor to make a travel route plan in an unfamiliar city, we study on how to evaluate a prospective scenery spot and recommend visitors with multiple constraints and diverse objectives. We first extract specific information of an attraction from the Tourism website, including the score graded by visitors, opening time, the price of an entrance ticket, and the GPS coordinates. Afterwards, we propose a kgreedy algorithm to generate a feasible trip recommendation with good performance, i.e. low cost and long stay time. Based on the datasets of the famous attractions in Beijing collected from the Lvping website, we implement and evaluate the proposed algorithm. Experimental results show that it can provide accurate and reasonable trip plans for users with diverse requirements.

Key words: scoring mechanism;comprehensive information of tourist attraction;optimal route plan

陆国锋1，黄晓燕2，吕绍和1，王晓东1. 基于互联网信息的多约束多目标旅游路线推荐[J]. J4, 2016, 38(01): 163-170.

LU Guofeng1 ,HUANG Xiaoyan2 ,L Shaohe1 ,WANG Xiaodong1. Multi-constraint and multi-objective trip
recommendation based on internet information [J]. J4, 2016, 38(01): 163-170.

参考文献［11］

［1］	Dunstall S,Horn MET,Kilby P,et al. An automated itinerary planning system for holiday travel［J］. Information Technology and Tourism,2003,6(3):195210.
［2］	Ardissono L,Goy A,Petrone G,et al. A multiagent infrastructure for developing personalized webbased systems［J］.ACM Transactions on Internet Technology,2005,5(1):4769.
［3］	Kim J,Kim H,Ry JH. TripTip:A trip planning service with tagbased recommendation［C］∥Proc of CHI,2009:34673472.
［4］	Krumm J. Where will they turn:Predicting turn proportions at intersections［J］. Personal and Ubiquitous Computing,2010,13(7):591599.
［5］	Huang Y,Bian L.A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet［J］. Expert System with Application,2009,36(1):933943.
［6］	Chodhury M D,Feldman M,AmerYahia S,et al. Automatic construction of travel itineraries using social breadcrumbs［C］∥Proc of HT 2010,2010:3544.
［7］	Zheng Y,Zhang L,Xie X,et al. Mining correlation between locations using human location history［C］∥Proc of GIS 2009,2009:472475.
［8］	Abowd G D,Atkeson C G,Hong J,et al. Cyberguide:A mobile contextaware tour guide［J］. Wireless Networks,1997,3(5):421433.
［9］	Horozov T,Narasimhan N,Vasudevan V. Using location for personalized POI recommendations in mobile environments［C］∥Proc of SAINT,2006:124129.
［10］	Clements M,Serdyukov P,de Vries A P,et al. Personalized travel recommendation based on location cooccurrence［C］∥Proc of CoRR,abs/1106.5213,2011.
［11］	Lu E HC,Chen CY,Tseng V S. Personalized trip recommendation with multiple constraints by mining user checkin behaviors［C］∥Proc of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems,2012:209218.