Scalable user intent mining using a multimodal restricted boltzmann machine
First Claim
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.
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Abstract
A method for scalable user intent mining is provided. The method includes detecting named entities from a plurality of query logs in a public query log dataset and generating features of the plurality of query logs based on the detected named entities. The method also includes applying a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs to train a public multimodal RBM and generating a plurality of public query representations. Further, the method includes 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, user intent is predicted using the public multimodal RBM. When there are the history queries, the public multimodal RBM is applied on the plurality of history queries to train a personalized multimodal RBM, and the user intent is predicted using the personalized multimodal RBM.
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Citations
18 Claims
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1. A method for scalable user intent mining implemented by at least one processor, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for scalable user intent mining, comprising at least one processor, at least one memory, and at least one program stored in the memory and to be executed by the at least one processor, wherein the at least one processor executes the at least one program to:
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detect named entities from a plurality of query logs in a public query log dataset; generate corresponding features of the plurality of query logs based on the detected named entities; apply a multimodal restricted boltzmann machine (RBM) on the generated features of the plurality of query logs in the public query log dataset to generate a plurality of public query representations; apply the multimodal RBM on a plurality of history queries of the user to train a personalized multimodal RBM; and predict user intent using one of the public multimodal RBM and the personalized multimodal RBM, so that an accuracy of predicting the user intent is improved by using the personalized multimodal RBM. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A non-transitory computer readable storage medium storing computer-executable instructions to execute operations for scalable user intent mining, the computer-executable instructions comprising:
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detecting named entities from a plurality of query logs in a public query log dataset; based on the detected named entities, generating corresponding features of the plurality of query logs; applying a multimodal restricted boltzmann machine (RBM) on the generated 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. - View Dependent Claims (15, 16, 17, 18)
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Specification