Event Prediction Using Hierarchical Event Features
First Claim
1. A computer-implemented method comprising:
- monitoring a stream of events occurring at an apparatus, each event being associated with a plurality of features describing the event, at least some of the plurality of features being related in a hierarchical manner;
creating a graphical data structure comprising variable nodes connected by edges, the plurality of features describing the event being represented by variable nodes and the variable nodes being connected such that sequences of connected variable nodes represent the hierarchical relations between features, each variable node being associated with statistics describing a probability distribution representing a latent event score;
arranging a training engine to update the statistics for at least one of the variable nodes on the basis of the monitoring; and
predicting an event using the graphical data structure.
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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.
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Citations
20 Claims
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1. A computer-implemented method comprising:
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monitoring a stream of events occurring at an apparatus, each event being associated with a plurality of features describing the event, at least some of the plurality of features being related in a hierarchical manner; creating a graphical data structure comprising variable nodes connected by edges, the plurality of features describing the event being represented by variable nodes and the variable nodes being connected such that sequences of connected variable nodes represent the hierarchical relations between features, each variable node being associated with statistics describing a probability distribution representing a latent event score; arranging a training engine to update the statistics for at least one of the variable nodes on the basis of the monitoring; and predicting an event using the graphical data structure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 11, 12)
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- 9. The computer-implemented method 1, wherein at least some of the plurality of features for one or more of the events are not hierarchically related and the graphical data structure is created such that the graphical data structure comprises an observation component including a plurality of variable nodes representing weights associated with the features that are not hierarchically related.
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13. A system comprising:
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one or more processors; memory; a monitor component maintained at least in part in the memory and executed at least in part by the one or more processors to monitor a stream of events occurring at the system, each event being associated with a plurality of features describing the event, at least some of the plurality of features being related in a hierarchical manner; a graphical data component maintained at least in part in the memory and executed at least in part by the one or more processors to create a graphical data structure comprising layers of parent and child variable nodes connected by edges, weights associated with the features being represented by variable nodes and the variable nodes being connected such that sequences of connected variable nodes represent the hierarchical relations between features, each variable node being associated with statistics describing a probability distribution representing a latent event score; a training engine maintained at least in part in the memory and executed at least in part by the one or more processors to update the statistics for at least one of the variable nodes on the basis of the monitoring; and a prediction engine maintained at least in part in the memory and executed at least in part by the one or more processors to predict an event using the graphical data structure. - View Dependent Claims (14, 15, 16)
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17. One or more computer storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising:
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monitoring a stream of search and user input events, each event being associated with a plurality of features describing the event, at least some of the plurality of features being universal resource locator prefix levels of a document; creating a graphical data structure comprising layers of parent and child variable nodes connected by edges, weights associated with the plurality of features being represented by variable nodes and at least some of the variable nodes being connected such that sequences of connected variable nodes represent the universal resource locator prefix levels of the document, each variable node being associated with statistics describing a probability distribution representing a latent event score; updating the statistics for at least one of the variable nodes on the basis of the monitoring; and predicting a user input event using the graphical data structure. - View Dependent Claims (18, 19, 20)
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Specification