Abstract: Search result re-ranking is provided by employing a concept graph from a metabase. When a search query is received, a query context of the search query is analyzed to identify dominant concepts for the search query. The dominant concepts are expanded by identifying the dominant concepts within a concept graph and identifying additional concepts having a strong relationship with the dominant concepts within the concept graph. A set of search results for the search query is analyzed to determine strength of relationship of each search result to the expanded concepts. The search results are re-ranked based on the strength of relationship of each search result to the expanded concepts and the strength of relationship of each expanded concept to the dominant concepts.

Abstract: Search result re-ranking is provided by employing a concept graph from a metabase. When a search query is received, a query context of the search query is analyzed to identify dominant concepts for the search query. The dominant concepts are expanded by identifying the dominant concepts within a concept graph and identifying additional concepts having a strong relationship with the dominant concepts within the concept graph. A set of search results for the search query is analyzed to determine strength of relationship of each search result to the expanded concepts. The search results are re-ranked based on the strength of relationship of each search result to the expanded concepts and the strength of relationship of each expanded concept to the dominant concepts.

Abstract: Advertisements are selected for presentation on a search results page based on the presence or absence of user activity within the search results page. When a search results page is provided to an end user, the search results page is tracked for user activity. If user activity is detected, a user context is identified and new advertisements are selected for presentation on the search results page based on the user context. In some embodiments, the search results page provides search results in clusters, and the user context corresponds with a search result cluster selected by an end user. In some embodiments, dominant concepts in the user context are identified and used to select advertisements. Alternatively, if user activity is not detected within a predetermined period of time, new advertisements are selected based on the absence of user activity within the search results page.

Abstract: Computer-readable media, systems, and methods for integrating advertisements using encapsulated advertisement controls are described. In embodiments, one or more embedding instructions are received for embedding one or more encapsulated advertisement controls within an application, the one or more encapsulated advertisement controls including logic for handling of one or more advertisements and presentation of the advertisements to a user of the application. Further, in embodiments, one or more configuration instructions are received for configuring the one or more encapsulated advertisement controls. Still further, in embodiments, one or more advertisements are presented to a user of the application in accordance with the one or more advertisement presentation parameters.

Abstract: Advertisement response probabilities are utilized to alter advertisement scores. A plurality of possible advertisements is accessed from, for example, an advertisement database or advertisement pipeline. A response probability for each advertisement is determined. A response probability may be a probability that a user will “click,” or otherwise select an advertisement. Advertisements may be associated with probabilistic prediction models that take advertisement recipient attribute values as inputs and provide a probability distribution as output. A score associated with each of the possible advertisements is altered based on the response probability for each of the advertisements. Statistical prediction is used to determine how scores are to be altered. Advertisements with response probabilities less than a mean probability may have associated scores decreased. Conversely, advertisements with response probabilities greater than a mean probability may have associated scores increased.

Abstract: Advertisement response probabilities are utilized to alter advertisement scores. A plurality of possible advertisements is accessed from, for example, an advertisement database or advertisement pipeline. A response probability for each advertisement is determined. A response probability may be a probability that a user will “click,” or otherwise select an advertisement. Advertisements may be associated with probabilistic prediction models that take advertisement recipient attribute values as inputs and provide a probability distribution as output. A score associated with each of the possible advertisements is altered based on the response probability for each of the advertisements. Statistical prediction is used to determine how scores are to be altered. Advertisements with response probabilities less than a mean probability may have associated scores decreased. Conversely, advertisements with response probabilities greater than a mean probability may have associated scores increased.