Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content items are disclosed. In one aspect, a method includes accessing a scaling factor value and accessing a first page value range specifying at least a high page value and a low page value. A determination is made that a first ranking of content items based on the high page value does not match a second ranking of the content items that is based on the low page value. In response to determining that the first ranking does not match the second ranking, an updated first ranking and an updated second ranking are determined based on a second page value range. A determination is made that the updated first ranking matches the updated second ranking. Content items are distributed based on the updated first ranking.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for ranking content. A request for content is received. Eligible content items are identified, including a first eligible content item for which an uncertainty level of an associated expected click-through rate is above a predefined threshold. A subset of the eligible content items is evaluated, including the first eligible content item including producing a score. The score is a product of an associated bid and click-through rate for a given eligible content item. Producing the score includes adjusting a product of a bid times an expected click-through rate for the first eligible content item by a value of learning that represents a value for exploring the first eligible content item as a response to the request. The subset of eligible content items is ranked based on the produced scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for ranking content items. In one aspect, a method includes identifying, for a content item, a bid value specifying an amount a content item provider is willing to pay for user interaction with the content item. A predicted performance measure is identified for the content item. The predicted performance measure is adjusted based on a weighting factor for the content item. The weighting factor for the content item is indicative of confidence that the predicted performance measure will match an actual performance measure for the content item and can be different than a weighting factor for another content item identified for inclusion in a ranking with the content item. A rank score is determined for the content item using the bid value and adjusted predicted performance measure. The content item is provided based on the rank score.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a content item. In one aspect, a method includes receiving a content item request. A set of candidate content items that are eligible to be provided in response to the content item request is identified. A performance measure is predicted for each candidate content item based at least in part on a loss function that specifies an economic cost of incorrectly predicting the performance measure for the candidate content item. The loss function can be based in part on a distribution of competing bid values for a set of previous content item impressions. A candidate content item can be selected for presentation based on the predicted performance measure for the candidate content items. The selected candidate content item is provided in response to the content item request.

Abstract: Community-based rating information is generated about a Web site, Web page or other network-accessible content for use in Web filtering operations. The rating information may relate to the appropriateness of the content for a particular audience or audiences, such as for children or for children of different age groups. The rating information is based on feedback provided by users who have accessed the content in question. Where the group of users providing feedback is sufficiently large, the rating assigned to the content will tend to accurately reflect community standards. Also, because the rating information is based on user feedback, the rating information can change over time to reflect changing community attitudes towards content.

Abstract: Community-based rating information is generated about a Web site, Web page or other network-accessible content for use in Web filtering operations. The rating information may relate to the appropriateness of the content for a particular audience or audiences, such as for children or for children of different age groups. The rating information is based on feedback provided by users who have accessed the content in question. Where the group of users providing feedback is sufficiently large, the rating assigned to the content will tend to accurately reflect community standards. Also, because the rating information is based on user feedback, the rating information can change over time to reflect changing community attitudes towards content.