Nowadays, artificial neural networks are widely used for users’ online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users’ intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users’ intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users’ intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users’ actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning


    Contributors:
    Li, Yu (author) / Xiong, Fei (author) / Wang, Ziyi (author) / Chen, Zulong (author) / Xu, Chuanfei (author) / Yin, Yuyu (author) / Zhou, Li (author)


    Publication date :

    2022-03-01


    Size :

    3838262 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Benefits of Multi-Destination Travel Planning for Electric Vehicles

    Cuchy, Marek / Stolba, Michal / Jakob, Michal | IEEE | 2018



    Destination and travel information application

    RICCI CHRISTOPHER P | European Patent Office | 2015

    Free access

    Combining spatial and temporal dimensions in destination choice models

    Cirillo, C. / Cornelis, E. / Legrain, L. et al. | British Library Conference Proceedings | 2003