A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Methods and systems for optimizing campaign delivery of messages, such as offers or incentives, are provided. A set of statistical and learning models identify similar campaigns, and generate recommendations for the current campaign based on past performance as measured by engagement with and performance of identified previous campaigns. An optimization tool may be used in conjunction with an offer distribution platform that identifies individual user groups, and develops recommended offers to be included within the campaign for use with specific users or user groups to achieve optimized results within provided campaign objectives.

Embodiments of the present invention disclose a push information pre-selecting method and apparatus. The method includes: based on historical push data of push information including a plurality of push information items, determining a feature for calculating a predicted value, and a weight corresponding to the feature; calculating a standard deviation of the feature; determining a fluctuation probability of the standard deviation; calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability, the standard deviation and the fluctuation probability being used for calculating a fluctuation value for correcting the weight; based on the predicted value, selecting push information items satisfying a preset condition; and pushing the selected push information items to a target user.

An application framework for mobile devices may provide a variety of application modules directed towards enterprise brand extension. The application modules are organized into five main categories: (1) featured, (2) community, (3) play/engage, (4) media, and (5) shop. The featured category may allow enterprises to push specific content onto its consumers. The community category may allow enterprises to leverage social networks and consumer communities that build and expand around their brands. The play/engage category may allow enterprises to offer compelling value and engaging utility to its customers. The media category may allow enterprises to entertain, inform, and educate consumers about brands through media content. The shop category may allow enterprises to facilitate electronic commerce with its customers. Further application analytics may be utilized by aggregating affiliate, sales, or usage data, etc. to better drive new revenue streams and optimize the return on investment associated with sales, promotion and advertising efforts.

An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are associated with a predicted marginal effect on a performance metric associated the optimal content item. This marginal effect may be predicted using a machine-learned model that is trained using historical performance information about content items that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user and one or more features associated with candidate components included in these content items and in the optimal content item.

The systems and methods described herein may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input at least one of: a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, a share of wallet, and/or the like.

In one aspect, a request for web content is received from a user device communicatively coupled to the processing device via the network. In response to receiving the request, user information associated with the user is determined. Predicted responses of the user to each variation of a plurality of variations of the web content are determined using prediction models and the user information. The prediction models include one or more decision trees generated using a splitting criterion requiring a minimum number of positive responses to a variation and a minimum number of negative responses to the variation as a condition of considering the possible split. The variation determined to have a threshold likelihood of yielding a predicted positive response of the predicted responses is selected based on the user information. The variation is transmitted to the user device via the network.

An online system selecting content items for presentation to its users accounts for likelihoods of users performing actions associated with content items when selecting content items. The online system maintains models determining likelihoods of users performing various actions. If a content item is associated with an action that infrequently occurs, information for determining the model for the action is limited, so the online system increases a bid amount associated with the content item during a time interval to an amount based on a likelihood of the user performing a more frequently occurring alternative action and an average bid amount for the alternative action from content items previously presented to users. The online system also determines an amount based on the model for the action and the bid amount for during the time interval and stops increasing the bid amount when the rate of change has less than a threshold magnitude.

Methods and systems for determining an individual gaze value are disclosed herein. An exemplary method involves: (a) receiving gaze data for a first wearable computing device, wherein the gaze data is indicative of a wearer-view associated with the first wearable computing device, and wherein the first wearable computing device is associated with a first user-account; (b) analyzing the gaze data from the first wearable computing device to detect one or more occurrences of one or more advertisement spaces in the gaze data; (c) based at least in part on the one or more detected advertisement-space occurrences, determining an individual gaze value for the first user-account; and (d) sending a gaze-value indication, wherein the gaze-value indication indicates the individual gaze value for the first user-account.