Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for evaluating a creative. A method includes: identifying one or more selection keywords that are associated with a creative, the selection keywords used in determining when the creative is shown in response to received content requests that include one or more request keywords; identifying matching criteria for comparing the selection keywords and the request keywords, the matching criteria defined by a range from an exact match to a broad match and including a matching threshold in the range; establishing a bid modifier associated with the matching criteria, the bid modifier modifying a bid associated with the creative based on an extent of a match determined between the selection keywords and the request keywords for a given content request; and evaluating the creative responsive to received content requests based on the matching criteria and the bid modifier.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for evaluating a creative. Methods can include identifying one or more selection keywords that are associated with a creative. Matching criteria can be identified for comparing the selection keywords and the request keywords. The matching criteria can be defined by a range from an exact match to a broad match and include a matching threshold in the range. A bid modifier associated with the matching criteria can be established. The bid modifier can modify a bid associated with the creative based on an extent of a match determined between the selection keywords and the request keywords for a given content request. The creative responsive to received content requests can be evaluated based on the matching criteria and the bid modifier.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for evaluating a creative. A method includes: identifying one or more selection keywords that are associated with a creative, the selection keywords used in determining when the creative is shown in response to received content requests that include one or more request keywords; identifying matching criteria for comparing the selection keywords and the request keywords, the matching criteria defined by a range from an exact match to a broad match and including a matching threshold, in the range; establishing a bid modifier associated with the matching criteria, the bid modifier modifying a bid associated with the creative based on an extent of a match determined between the selection keywords and the request keywords for a given content request; and evaluating the creative responsive to received content requests based on the matching criteria and the bid modifier.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for comparing a request entity to a campaign entity. A method includes: identifying a campaign associated with a content sponsor, the campaign having an associated creative for presentation to users and selection criteria for use in evaluating when to present the creative in response to received requests for content; receiving at least one keyword from the content sponsor to be used as selection criteria; identifying an entity from a plurality of entities to associate with the keyword; storing the identified entity along with the keyword in association with the campaign; and using the keyword and the identified entity when evaluating the received requests for content.

Abstract: This specification describes technologies relating to displaying online content. In general, one aspect of the subject matter described in this specification can be embodied in methods that include receiving a first query associated with a user request. The methods may further include determining a score for a word in the first query based at least in part on user interaction with a content item served for display in response to a past query that includes the word. The methods may further include selecting a keyword derived from the first query based at least in part on the score. The methods may further include identifying candidate content items using the selected keyword. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.

Abstract: Methods, systems, and apparatus, including computer program products, for selecting resources associated with a common topic. In one aspect, a method includes selecting a first resource associated with a topic, the first resource accessed in a user session, selecting a second resource accessed during the user session, determining whether the second resource is associated with the topic, and increasing a relevance score of the second resource and the topic based on determining that the second resource is not associated with the topic.

Abstract: A query classification system classifies queries based on query features of search queries and a query classification model. The query classification model can be trained to classify a search query based on the query features independent of (e.g., without accessing) search results or other search log data for the search query. The query classification model can be trained based on query data and search log data corresponding to a plurality of labeled queries.

Abstract: One embodiment of the present invention provides a system that produces an object signature for an object, wherein the object comprises a set of features. During operation, the system first initializes a k-dimensional state vector {s1, s2, . . . , sk}containing floating-point numbers. Next, for each occurrence of each feature in the object, the system (1) computes a random-number seed from the feature; (2) generates k pseudo-random floating-point numbers {X1, X2, . . . , Xk} using the random-number seed, wherein each Xi(i?[1, k]) is generated in accordance with an ?-stable distribution, wherein 1??<2; and (3) updates each floating-point number si in the k-dimensional state vector using an associated pseudo-random floating-point number Xi. The system then produces the object signature for the object by condensing the k-dimensional state vector.