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.
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. 3. The method according to claim 1 , wherein applying the public multimodal RBM on the plurality of history queries of the user to train a personalized multimodal RBM further includes: detecting named entities from the plurality of history queries of the user; based on the detected named entities, generating corresponding features of the plurality of history queries of the user; generating at least one personal query representation of the plurality of history queries of the user using the public multimodal RBM; based on the at least one personal query representation, comparing the at least one personal query representation with the plurality of public query representations to find one or more relevant users that share at least one similar interest with the user; and training the public multimodal RBM using data from the one or more relevant users to generate the personalized multimodal RBM.