Systems, methods, and computer-readable media are provided that help advertisers identify and bid for valuable display advertising impressions made available through advertising exchanges. An influence determination system builds an influence graph that includes representations of entities that make advertising impressions available and interactions between such entities. The influence determination system determines contexts relevant to the entities, and applies context labels to entities in the influence graph as appropriate. The influence determination system calculates and stores context-sensitive influence scores for entities in the influence graph. The context labels and context-sensitive influence scores may be used by advertisers to choose advertising impressions on which bids will be placed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for identifying contexts associated with entities using a supervised learning process, the system comprising at least one computing device configured to provide: an example data store configured to store training examples, wherein each training example includes one or more contexts assigned to an entity; a management interface engine configured to present an interface for managing the supervised learning process; a training engine configured to build one or more classification models based on training examples stored in the example data store; and a classification engine configured to identify contexts associated with entities using the one or more classification models built by the training engine. 2. The system of claim 1, wherein presenting an interface for managing the supervised learning process includes: presenting training examples of annotated entities organized by context; and receiving an instruction to delete a presented training example or to assign a training example to a different context. 3. The system of claim 1, wherein presenting an interface for managing the supervised learning process includes presenting a search interface that allows new training examples for a given context to be found using keyword-based web search queries. 4. The system of claim 1, wherein presenting an interface for managing the supervised learning process includes presenting a search interface that allows new training examples to be found using keyword-based search queries of entities annotated with contexts by a classification model. 5. The system of claim 4, wherein the search interface includes options to filter by one or more of a given context and a classification confidence. 6. The system of claim 1, wherein presenting an interface for managing the supervised learning process includes presenting a feedback interface that includes one or more of: a browse interface for browsing samples of classifications produced by a classification model; a test result interface for viewing test results for individual contexts, wherein the test results may include one or more of recall, precision, f-score, and a confusion matrix; and a context discovery interface configured to present clusters of features from low-confidence classifications. 7. A computer-implemented method of predicting contexts associated with entities, the method comprising: applying, by a computing device, a plurality of classification models to an entity, wherein each classification model of the plurality of classification models is associated with a separate context, and wherein the output of each classification model is a context probability indicating a probability that the entity is associated with the context of the classification model; choosing, by a computing device, a context associated with a highest context probability; storing, by a computing device, a context label associated with the entity, wherein the context label identifies the context associated with the highest context probability and includes the context probability of the context; and in response to determining that the context probability of the context is less than a probability threshold and that uncertainty of a context distribution is greater than an uncertainty threshold, storing, by a computing device, an indication in the context label that the classification is a low confidence classification. 8. The computer-implemented method of claim 7, wherein the uncertainty of the context distribution is determined by calculating a Shannon Entropy of a probability distribution of determined probabilities for each of the plurality of classification models. 9. A computer-readable medium having computer-executable instructions stored thereon that, in response to execution by one or more processors of one or more computing devices, cause the one or more computing devices to determine influence of an entity by: generating, by the one or more computing devices, an interaction graph, wherein vertices in the graph represent entities, wherein the vertices include weighted attributes indicating associated contexts, and wherein directed edges in the graph represent interactions between entities; and calculating, by the one or more computing devices, Eigenvector centrality for the graph to determine influence scores for each vertex. 10. The computer-readable medium of claim 9, wherein calculating Eigenvector centrality includes performing a Monte Carlo method to probabilistically estimate a rank vector. 11. The computer-readable medium of claim 10, wherein performing the Monte Carlo method includes optimizing the Monte Carlo method by staggering walk starts. 12. The computer-readable medium of claim 10, wherein performing the Monte Carlo method includes optimizing the Monte Carlo method by parallelizing calculation of each context rank vector. 13. The computer-readable medium of claim 9, wherein edges of the graph are weighted based on an age of the interaction. 14. The computer-readable medium of claim 9, wherein edges of the graph are weighted based on context similarity of the sink vertex. 15. The computer-readable medium of claim 9, wherein the computer-readable instructions further cause the one or more computing devices to discount an influence of an entity associated with a vertex based on a Shannon Entropy of the entity's influence distribution for a plurality of contexts associated with the entity. 16. The computer-readable medium of claim 9, wherein the computer-readable instructions further cause the one or more computing devices to scale influence scores to form a human-consumable representation of influence using a logistical function, a linear function, or a cumulative probability density function.