Personalized tourist route recommendation model with a trajectory understanding via neural networks  

作　　者：Naixia Mou Qi Jiang Lingxian Zhang Jiqiang Niu Yunhao Zheng Yanci Wang Tengfei Yang 

机构地区：[1]College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao,People’s Republic of China [2]School of Geographic Sciences,Xinyang Normal University,Xinyang,People’s Republic of China [3]Institute of Remote Sensing and Geographical Information Systems,School of Earth and Space Sciences,Peking University,Beijing,People’s Republic of China [4]Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2022年第1期1738-1759,共22页国际数字地球学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China (42171460);the Open Fund of Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution,Xinyang Normal University (KLSPWSEP-A09).

摘　　要：Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features,for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning,a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically,a trajectory encoding module is designed to mine the semantic information of trajectory data,and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular,a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai,and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

关 键 词：Recommendation system travel trajectory recurrent neural networks Flickr geotagged photos 

分 类 号：F59[经济管理—旅游管理]