Highlights Constructing travel mode recommendation system for the multi-modal transportation system. Transforming nodes in the bipartite graph into feature vectors using graph embedding technology. Post-processing technique to handle the inconsistency between the objective function and the evaluation metric.

    Abstract The emergence of navigation applications with multi-modal trip planning services has brought about the demand for the multi-modal transportation recommendation systems. In this paper, we explore the problem of large-scale multi-modal transportation recommendation and propose a novel travel mode recommendation system for a multi-modal transportation system. In the proposed model, the feature engineering focuses on the application scenario of the multi-modal transportation recommendation, and is designed from multiple perspectives of users, travel modes, locations, and time. To learn a better representation of the co-occurrence, we construct a bipartite graph for the Origin-Destination (OD) pair and the User-OD pair of all the query records then transformed nodes in the bipartite graph to feature vectors using a graph-embedding technique. Finally, we propose a post-processing technique to handle the inconsistency between the objective function and evaluation metric. Experimental results from a city-wide multi-modal transportation recommendation indicate that our proposed model is superior to the existing method of navigation service providers.


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    Titel :

    Exploring a large-scale multi-modal transportation recommendation system


    Beteiligte:
    Liu, Yang (Autor:in) / Lyu, Cheng (Autor:in) / Liu, Zhiyuan (Autor:in) / Cao, Jinde (Autor:in)


    Erscheinungsdatum :

    2021-02-25




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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