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.
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. 5. The method according to claim 3 , wherein 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 further includes: based on the at least one personal query representation, calculating similar named entities with the plurality of queries in the public query log dataset; based on the calculated similar named entities, collecting identifications of sections of the plurality of queries with significant similar named entities, wherein the identifications of the sections are regarded as the relevant users having the at least one similar interest with the user; and based on the collected identifications of the sections, building a new dataset including the plurality of queries from the one or more relevant users and the plurality of history queries from the user.