Abstract: A system for analyzing risk using machine learning models may be trained using a data set to generate a risk assessment model that is optimized for metrics commonly used in for financial risk evaluation. The metrics may include Gini and CaptureRate, for example. The system may receive a request for a financial service, and generate a risk assessment by applying the risk assessment model to factors associated with the request. The system may also decide on the request in response to the risk assessment.

Abstract: Online learning of click-through rates on search result blocks from one or more federated sources may be provided. Click-through feedback for the search result blocks may be received from the one or more federated sources in response to a query. Weights may be assigned to each of the search result blocks based on the received click-through feedback. The search result blocks may then be ranked based on the assigned weights. Finally, a search results page may be generated for displaying the ranked search results blocks to a user.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system.

Abstract: Online learning of click-through rates on search result blocks from one or more federated sources may be provided. Click-through feedback for the search result blocks may be received from the one or more federated sources in response to a query. Weights may be assigned to each of the search result blocks based on the received click-through feedback. The search result blocks may then be ranked based on the assigned weights. Finally, a search results page may be generated for displaying the ranked search results blocks to a user.

Abstract: A custom search ranking model is configured using a base ranking model that is combined with one or more additional ranking features. A base ranking model that has already been configured and tuned is selected that serves as the base ranking model for a custom search ranking model. The additional ranking feature(s) to combine with the base ranking model may be manually/automatically identified. For example, a feature selection algorithm may be used to automatically identify ranking features that are likely to have a positive impact on results provided by the base search ranking model. A user may also know of the ranking feature(s) that they would like to add to the base ranking model. The custom search ranking model may also be evaluated by automatically creating a set of virtual queries for evaluation.

Abstract: An outgoing message monitor is provided. In an embodiment, outgoing messages are monitored to detect potential errors and alerts may be triggered. Using information about a message such as an email, a first classifier classifies the email into an expected class and a second classifier classifies the email into an actual class. On the basis of a comparison of the expected and actual classes an alert may be triggered. In an embodiment, the second classifier uses information derived from text content of the email which may optionally be pre-processed. The first classifier, for example, uses other information about the email such as its intended recipients, information about the presence of attachments, information about whether the email is part of a thread and other information.

Abstract: Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system.

Abstract: Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system.

Abstract: Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system.

Abstract: An outgoing message monitor is provided. In an embodiment, outgoing messages are monitored to detect potential errors and alerts may be triggered. Using information about a message such as an email, a first classifier classifies the email into an expected class and a second classifier classifies the email into an actual class. On the basis of a comparison of the expected and actual classes an alert may be triggered. In an embodiment, the second classifier uses information derived from text content of the email which may optionally be pre-processed. The first classifier, for example, uses other information about the email such as its intended recipients, information about the presence of attachments, information about whether the email is part of a thread and other information.