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

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.

Automated summary

This study proposes a personalized recurrent neural network (P-RecN) for tourist route recommendation. By mining the semantic information of historical trajectory data and capturing the sequence travel patterns of tourists, the model can better understand the travel patterns of tourists, improving recommendation accuracy and ranking ability.