1. A method for scalable user intent mining implemented by at least one processor, comprising: detecting named entities from a plurality of query logs in a public query log dataset, wherein the public query log dataset stores the plurality of query logs from a plurality of websites; based on the detected named entities, generating corresponding features of the plurality of query logs; applying a multimodal restricted boltzmann machine (RBM) on the corresponding features of the plurality of query logs to train a public multimodal RBM; generating a plurality of public query representations; receiving a search query from a user; determining whether there are a plurality of history queries of the user; when there is no history query of the user, predicting user intent using the public multimodal RBM; and when there are the plurality of history queries of the user, applying the public multimodal RBM on the plurality of history queries of the user to train a personalized multimodal RBM, and predicting the user intent using the personalized multimodal RBM, so that an accuracy of predicting the user intent is improved by using the personalized multimodal RBM.