In order to solve the problems of considering only one transportation mode and neglecting user preference in transportation recommendation problem

and class imbalance problem in multi-class task

a context-aware multi-modal transportation recommendation method based on particle swarm optimization and LightGBM is proposed. This method comprehensively considers the user’s travel preferences in terms of time

space and travel cost

and uses mathematical statistics and representation learning methods to capture the internal relationship between user travel and various elements. At the same time

in order to alleviate the negative impact caused by the imbalance of sample class

the index optimization method based on particle swarm optimization algorithm is used to search for the optimal weight for each class

and the prediction results of the model are modified to achieve the purpose of maximizing the evaluation index. Experimental results show that compared with traditional algorithms

the model proposed in this paper has better performance in spatio-temporal feature extraction

alleviating class imbalance and recommendation accuracy.