According to one embodiment, a first set of features is received, where each of the features in the first set being associated with a predetermined category. A bloom filter is applied to the first set of features to generate a second set of features. A neural network model is trained by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection. The output of the neural network model is compared with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for generating neural network models, the method comprising: receiving a first set of features, each of the features in the first set being associated with a predetermined category; applying a bloom filter to the first set of features to generate a second set of features; training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection; and comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition. 2. The method of claim 1, wherein the first set of features includes a first number of features that is greater than a second number of features included in the second set of features. 3. The method of claim 2, further comprising: adjusting one or more parameters of the bloom filter in response to determining that the neural network model does not satisfy the predetermine condition; applying the bloom filter with adjusted parameters to the first set of features to generate a third set of features, wherein the third set of features includes a third number of features different than the second number of features in the second set; and training the neural network model based on the third set of features. 4. The method of claim 1, wherein each of the features in the second set represents a combination of two or more features in the first set. 5. The method of claim 1, further comprising: in response to a search query, performing in a content database to identify a list of content items based on the search query; for each of the content items, determining a score for the content item using the neural network model based; ranking the content items based on scores associated with the content items; and generating a search result having the content items sorted based on the rankings. 6. The method of claim 5, wherein determining a score for the content item using the neural network model based comprises: obtaining a third set of features associated with the content item; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the score. 7. The method of claim 1, further comprising: receiving a search query from a user device of a user; determining a category of the user using the neural network model based on at least the search query; and performing a search in a content database based on search query and the category of the user. 8. The method of claim 7, wherein determining a category of the user using the neural network model comprises: obtaining a third set of features based on the search query, device information of the user device, and user information of the user; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the category of the user. 9. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of training classification models, the operations comprising: receiving a first set of features, each of the features in the first set being associated with a predetermined category; applying a bloom filter to the first set of features to generate a second set of features; training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection; and comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition. 10. The non-transitory machine-readable medium of claim 9, wherein the first set of features includes a first number of features that is greater than a second number of features included in the second set of features. 11. The non-transitory machine-readable medium of claim 10, wherein the operations further comprise: adjusting one or more parameters of the bloom filter in response to determining that the neural network model does not satisfy the predetermine condition; applying the bloom filter with adjusted parameters to the first set of features to generate a third set of features, wherein the third set of features includes a third number of features different than the second number of features in the second set; and training the neural network model based on the third set of features. 12. The non-transitory machine-readable medium of claim 9, wherein each of the features in the second set represents a combination of two or more features in the first set. 13. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise: in response to a search query, performing in a content database to identify a list of content items based on the search query; for each of the content items, determining a score for the content item using the neural network model based; ranking the content items based on scores associated with the content items; and generating a search result having the content items sorted based on the rankings. 14. The non-transitory machine-readable medium of claim 13, wherein determining a score for the content item using the neural network model based comprises: obtaining a third set of features associated with the content item; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the score. 15. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise: receiving a search query from a user device of a user; determining a category of the user using the neural network model based on at least the search query; and performing a search in a content database based on search query and the category of the user. 16. The non-transitory machine-readable medium of claim 15, wherein determining a category of the user using the neural network model comprises: obtaining a third set of features based on the search query, device information of the user device, and user information of the user; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the category of the user. 17. A data processing system, comprising: a processor; and a memory coupled to the processor, the memory storing instructions, which when executed by the processor, cause the processor to perform operations of training classification models, the operations including receiving a first set of features, each of the features in the first set being associated with a predetermined category, applying a bloom filter to the first set of features to generate a second set of features, training a neural network model by applying the second set of features to a first layer of nodes of the neural network model to generate an output, the neural network model including a plurality of layers of nodes coupled to each other via a connection, and comparing the output of the neural network model with a target value associated with the predetermined category to determine whether the neural network model satisfies a predetermined condition. 18. The system of claim 17, wherein the first set of features includes a first number of features that is greater than a second number of features included in the second set of features. 19. The system of claim 18, wherein the operations further comprise: adjusting one or more parameters of the bloom filter in response to determining that the neural network model does not satisfy the predetermine condition; applying the bloom filter with adjusted parameters to the first set of features to generate a third set of features, wherein the third set of features includes a third number of features different than the second number of features in the second set; and training the neural network model based on the third set of features. 20. The system of claim 17, wherein each of the features in the second set represents a combination of two or more features in the first set. 21. The system of claim 17, wherein the operations further comprise: in response to a search query, performing in a content database to identify a list of content items based on the search query; for each of the content items, determining a score for the content item using the neural network model based; ranking the content items based on scores associated with the content items; and generating a search result having the content items sorted based on the rankings. 22. The system of claim 21, wherein determining a score for the content item using the neural network model based comprises: obtaining a third set of features associated with the content item; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the score. 23. The system of claim 17, wherein the operations further comprise: receiving a search query from a user device of a user; determining a category of the user using the neural network model based on at least the search query; and performing a search in a content database based on search query and the category of the user. 24. The system of claim 23, wherein determining a category of the user using the neural network model comprises: obtaining a third set of features based on the search query, device information of the user device, and user information of the user; applying the bloom filter to the third set of features to generate a fourth set of features; and applying the neural network model to the fourth set of features to generate the category of the user.