本文主要解决了，给定一个用户，和OD（起点和终点），为用户推荐出行方式，如步行，自行车，bus等。
具体做法，构建了user-OD-model三层关系的网络结构，最后通过metric learning（trans2vec/embedding learning）的方法，学出这三者的表达，然后通过距离的方式，做出推荐。
其中有意思的是，构建关系网络的时候，本文先根据经验计算关联，然后通过这个已知关联，去训练embeddings。再把embeddings用到其他关系的训练当中。这里面涉及到了负采样，回归学表达，求近邻等技术。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Joint Representation Learning for Multi-Modal Transportation Recommendation | |||||||||||
| 文献作者 | Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| trans2vec; metric learning; graph; embedding; | |||||||||||
| 摘要描述 | |||||||||||
| Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multi-modal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multi-modal transportation recommendations. | |||||||||||
