A method and apparatus of a device that generates a re-ranking model used to re-rank a plurality of search results on a client device is described. In an exemplary embodiment, the device receives a crowd-sourced intra-domain model from a server, where the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices. The device further generates a re-ranking model from the crowd-sourced intra-domain model and a local model, where the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine-readable medium having executable instructions to cause one or more processing units to perform a method to generate a re-ranking model used to re-rank a plurality of search results on a client device, the method comprising: receiving a crowd-sourced intra-domain model from a server, wherein the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices; and generating a re-ranking model from the crowd-sourced intra-domain model and a local model, wherein the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results. 2. The machine-readable medium of claim 1, wherein there are a plurality of intra-domain models corresponding to a plurality of search domains that are used to generate the plurality of search results. 3. The machine-readable medium of claim 2, further comprising: receiving a crowd-sourced inter-domain model from the server, wherein the crowd-sourced inter-domain model indicates which of the plurality of search domains are to be ranked higher than others in the plurality of search domains; and further generating the re-ranking model from the crowd-sourced inter-domain model and the local model and the crowd-sourced inter-domain model is used to blend the plurality of search results from the plurality of search domains. 4. The machine-readable medium of claim 1, wherein a choice of the crowd-source intra-domain model is based on at least a local associated with the client device. 5. The machine-readable medium of claim 1, further comprising: sending a set of raw features to the server, wherein the set of raw features includes a characteristic of an interaction with the client device by a user of that client device. 6. The machine-readable medium of claim 5, wherein the set of raw features does not reveal private data about the client device user. 7. The machine-readable medium of claim 1, wherein the re-ranking model is a neural network model. 8. The machine-readable medium of claim 7, wherein the neural network model is trained based on at least the set of raw features. 9. A machine-readable medium having executable instructions to cause one or more processing units to perform a method to generate a crowd-source model used to re-rank a plurality of search results on a client device, the method comprising: receiving raw features from a plurality of devices, wherein the raw features are data that indicates how users of the plurality of devices use the device and the raw features do not reveal private data about the user; and generating a crowd-sourced model from the raw features, wherein the crowd-sourced model includes a crowd-sourced intra-domain model used to re-rank search results of a search query for one of a plurality of search domains and a crowd-sourced inter-domain used to re-rank the plurality of search domains providing the search results of the search query; and sending the crowd-sourced model to the client device in response to the client device requesting the crowd-sourced model. 10. The machine-readable medium of claim 9, wherein the client device request is an opt-in by the client device to receive the crowd-sourced model. 11. The machine-readable medium of claim 10, wherein the client device opt-in request is an opt-in request to allow diagnostic data collection. 12. A method used to re-rank a plurality of search results on a client device, the method comprising: receiving a crowd-sourced intra-domain model from a server, wherein the intra-domain model is a search result re-ranking model generated based on at least device interactions of a plurality of users interacting with a plurality of other devices; and generating a re-ranking model from the crowd-sourced intra-domain model and a local model, wherein the local model includes private data representing a device user's interaction with that device and the re-ranking model is used to re-rank a plurality of search results. 13. The method of claim 12, wherein there are a plurality of intra-domain models corresponding to a plurality of search domains that are used to generate the plurality of search results. 14. The method of claim 13, further comprising: receiving a crowd-sourced inter-domain model from the server, wherein the crowd-sourced inter-domain model indicates which of the plurality of search domains are to be ranked higher than others in the plurality of search domains; and further generating the re-ranking model from the crowd-sourced inter-domain model and the local model and the crowd-sourced inter-domain model is used to blend the plurality of search results from the plurality of search domains. 15. The method of claim 13, further comprising: sending a set of raw features to the server, wherein the set of raw features includes a characteristic of an interaction with the client device by a user of that client device. 16. The method of claim 15, wherein the set of raw features does not reveal private data about the client device user. 17. The method of claim 12, wherein the re-ranking model is a neural network model. 18. A method to generate a crowd-source model used to re-rank a plurality of search results on a client device, the method comprising: receiving raw features from a plurality of devices, wherein the raw features are data that indicates how users of the plurality of devices use the device and the raw features do not reveal private data about the user; and generating a crowd-sourced model from the raw features, wherein the crowd-sourced model includes a crowd-sourced intra-domain model used to re-rank search results of a search query for one of a plurality of search domains and a crowd-sourced inter-domain used to re-rank the plurality of search domains providing the search results of the search query; and sending the crowd-sourced model to the client device in response to the client device requesting the crowd-sourced model. 19. The method of claim 18, wherein the client device request is an opt-in by the client device to receive the crowd-sourced model. 20. The method of claim 19, wherein the client device opt-in request is an opt-in request to allow diagnostic data collection.