The claimed subject matter includes a system and method for attribution of search activity in multi-user settings. The method includes training a classifier to distinguish between machines that are single-user and multi-user based on activity logs of an identified machine. The identified machine is determined to be multi-user based on the classifier. A number of users is estimated for the identified machine. Activity of the users is clustered based on the number of users estimated. A similarity function is learned for the number of users estimated. The method also includes assigning new activity to one of the users based on the clustering, and the similarity function.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for attribution of activity in multi-user settings, the method comprising: training a classifier to distinguish between machines that are single-user and multi-user based on activity logs of an identified machine; determining the identified machine is multi-user based on the classifier; estimating a number of users for the identified machine; clustering activity of the users based on the number of users estimated; learning a similarity function for the number of users estimated; and assigning new activity to one of the users based on the clustering, and the similarity function. 2. The method of claim 1, the activity logs comprising logs of search activity. 3. The method of claim 2, comprising making a recommendation for the one user based on the activity logs, the recommendation comprising one of a movie, book, song, video, or game. 4. The method of claim 1, the activity logs comprising online activities. 5. The method of claim 1, comprising presenting search results for the new activity based on the one user. 6. The method of claim 1, clustering comprising selecting features to classify search activity into clusters. 7. The method of claim 6, comprising classifying search activity based on temporal features of the search activity. 8. The method of claim 1, comprising allowing queries from multiple searchers on a shared machine to be hidden from other users. 9. The method of claim 1, comprising generating metrics for each of a plurality of users of the identified machine. 10. The method of claim 1, wherein assigning new activity comprises at least one of: applying blind source separation methods to the activity; Web site activity clustering; fraud detection; or diarization methods. 11. A system for attribution of search activity in multi-user settings, comprising: a processing unit; and a system memory, wherein the system memory comprises code configured to direct the processing unit to: train a classifier to distinguish between machines that are single-user and multi-user based on activity logs of an identified machine; determine the identified machine is multi-user based on the classifier; estimate a number of users for the identified machine; cluster activity of the users based on the number of users estimated; learn a similarity function for the number of users estimated; and assign new activity to one of the users based on the clustering, and the similarity function. 12. The system of claim 11, the system memory comprising code configured to direct the processing unit to make a recommendation for the one user. 13. The system of claim 12, the recommendation comprising one of a movie, book, song, video, or game. 14. The system of claim 11, the system memory comprising code configured to direct the processing unit to present a personalized advertisement for the one user 15. The system of claim 11, the system memory comprising code configured to direct the processing unit to present search results for the new activity based on the one user. 16. The system of claim 11, clustering comprising selecting features to classify search activity into clusters. 17. The system of claim 16, the system memory comprising code configured to direct the processing unit to classify search activity based on temporal features of the search activity. 18. One or more computer-readable storage memory devices for storing computer-readable instructions, the computer-readable instructions attributing search activity in multi-user settings when executed by one or more processing devices, the computer-readable instructions comprising code configured to: train a classifier to distinguish between machines that are single-user and multi-user based on activity logs of an identified machine; determine the identified machine is multi-user based on the classifier; estimate a number of users for the identified machine; cluster search activity of the users based on the number of users estimated; learn a similarity function for the number of users estimated; assign new activity to one of the users based on the clustering, and the similarity function; and make a recommendation for the one user, the recommendation comprising one of a movie, book, song, video, or game. 19. The computer-readable storage memory devices of claim 18, comprising code configured to direct the processing unit to present search results for the new activity based on the one user. 20. The computer-readable storage memory devices of claim 19, clustering comprising selecting features to classify search activity into clusters.