基于图嵌入和CaGBDT的多模态出行推荐

针对交通出行服务中推荐方式单一、容易忽略用户出行偏好等问题，借鉴多粒度级联森林结构，提出了一种级联梯度提升树模型（CaGBDT）.该模型利用级联结构增加模型的深度，进而实现了特征的深层次表示学习.同时，为了解决样本类别不平衡问题，提出了一种基于鲍威尔算法的指标优化层，其通过为每个类别搜索一个阈值，对模型的预测结果进行权重修正，以实现最大化评价指标的目的.此外，CaGBDT模型可以根据用户的出行记录，构建用户出行全局关系图，利用图嵌入表示学习方法，自动提取用户出行的空间上下文关系，从而提高特征提取的效率.

Multi-Modal Transportation Recommendation Based on Graph Embedding and CaGBDT

To solve the problems in transportation recommendation service,such as single recommended methods and ignoring the user travel preferences,a cascade gradient boosting decision tree (CaGBDT) model is proposed based on the multi-grained cascade forest structure. CaGBDT uses the cascade structure to increase the depth, and then realizes the deep-level representation learning of features. Meanwhile to solve the imbalance of sample class,an index optimization layer based on Powell algorithm is proposed. By searching a threshold for each class,the weight of the prediction results of the model is modified to maximize the evaluation indexes. In addition,a user travel global relationship graph can be constructed by the CaGBDT model via referring the user's travel record, and the spatial context relationship of the user's travel is extracted automatically by the graph embedding method to improve the efficiency of feature extraction.