Abstract: Profile information is maintained concerning users and files in the context of a file sharing and collaboration environment. Profile information quantifies each user's interest level in specific files, and levels of similarity between users and between files. Machine learning techniques are applied to monitored actions taken by users directed towards files, and profile information is dynamically updated in response. Natural language processing such as n-gram analysis is applied to files, and file similarity levels are increased in response to requisite amounts of common content. The event notification stream is filtered. For each specific event notification, a relevance value is quantified for each specific user, based on profile information concerning the specific user, the file to which the event is directed, and the user who undertook the event. The corresponding notification is only transmitted to specific users for whom the relevance value exceeds a predetermined threshold.

Abstract: Reputations of objects are determined by a reputation system using reports from clients identifying the objects. Confidence metrics for the clients are generated using information determined from the reports. Confidence metrics indicate the amounts of confidence in the veracity of the reports. Reputation scores of objects are calculated using the reports from the clients and the confidence metrics for the clients. Confidence metrics and reputation scores are stored in correlation with identifiers for the objects. An object's reputation score is provided to a client in response to a request.

Abstract: Reputations of objects are determined by a reputation system using reports from clients identifying the objects. Confidence metrics for the clients are generated using information determined from the reports. Confidence metrics indicate the amounts of confidence in the veracity of the reports. Reputation scores of objects are calculated using the reports from the clients and the confidence metrics for the clients. Confidence metrics and reputation scores are stored in correlation with identifiers for the objects. An object's reputation score is provided to a client in response to a request.

Abstract: A computer-implemented method to provide a secure uniform resource locator (URL) shortening service is described. A URL is received via a browser provided on a display of a computing device. A shortened URL is generated to represent the received URL. A determination is made as to whether a classification assigned to a web site associated with the shortened URL is valid. Upon determining that the assigned classification is not valid, the web site is evaluated in order to assign an updated valid classification to the web site.

Abstract: Reputations of objects are determined by a reputation system using reports from clients identifying the objects. Confidence metrics for the clients are generated using information determined from the reports. Confidence metrics indicate the amounts of confidence in the veracity of the reports. Reputation scores of objects are calculated using the reports from the clients and the confidence metrics for the clients. Confidence metrics and reputation scores are stored in correlation with identifiers for the objects. An object's reputation score is provided to a client in response to a request.