LearningTour: A Machine Learning Approach for Tour Recommendation Based on Users’ Historical Travel Experience

Abstract

Tour routes designning is a non-trival step for the tourists who want to take an excursion journey in someplace which he or she is not familiar with. For most tourists this problem represents an excruciating challenge due to such unfamiliarity. Few existing works focus on using other tourists’ experiences in the city to recommend a personalized route for the new comers. To take full advantage of tourists’ historical routes in route recommendation, we propose LearningTour, a model recommending routes by learning how other tourists travel in the city before. Giving that the tourist’s route is actually a special variance of time sequence, we treat such route as a special language and thus treat such recommendation process as a unique translation process. Therefore we use a sequence-to-sequence (seq2seq) model to proceed such learning and do the recommendation job. This model comprises a encoder and a decoder. The encoder encodes users’ interest to the context vector and the decoder decodes the vector to the generated route. Finally, we implemented our model on several real datasets and demonstrate its effeciency.

This work was supported by the National Key R&D Program of China [2018YFB1004703]; the National Natural Science Foundation of China [61872238, 61672353]; the Shanghai Science and Technology Fund [17510740200]; the Huawei Innovation Research Program [HO2018085286]; and the State Key Laboratory of Air Traffic Management System and Technology [SKLATM20180X].