A system and method for providing dynamic pay-for-placement advertisements via graphics-enabled email that generates a display of advertisements when the email newsletter is opened so the advertisements displayed are based on rankings at the time the email is opened instead of when the email was generated and transmitted. In one embodiment, a graphical-content email having one or more embedded advertisement image references is provided to one or more email recipients. The advertisement image reference, in one embodiment, may include query string parameters indicating the context of the image reference and/or portion of the image reference (i.e., identifying the image reference as being part of a particular newsletter email), a position of the image reference in the email display, and the like. A URL reference also may be included with each advertisement image reference (.e.g., one URL for each advertisement portion of the image to be retrieved by the advertising image reference).

A utility application for a mobile device inspects data packets from other mobile applications running on the device to gather and record usage data about those applications. Since users of the utility application may not reflect the true population for which the usage data is desired, a system de-biases the data reported from the utility applications using a machine learning model to predict demographics of the users of the utility application. To determine a training data set for the model, the system requests a user to provide a desired user attribute by way of an in-app questionnaire. This enables labeling utility usage data with the demographics, which can be weighted and extrapolated to determine usage across the population as a whole.

An e-commerce method involves on-line viewing of a first article through a linking node for virtual merging on another structure. A particular application of the invention is directed to on-line apparel shopping involving a color matching scheme using color codes provided with images to be merged. For example, on-line viewing of one article, such as clothing, on another structure, includes creating an item from image-data corresponding to a colored article selected by an on-line viewer from an on-line viewer site with an image of a colored structure selected by the on-line viewer, and indicating whether the colored article and the colored structure satisfy a color-matching criterion.

Methods and systems are provided that may be utilized to detect occurrence of one or more specified circumstances. A determination may be made as to whether one or more specified circumstances are detected such as responsive to one or more user actions or an occurrence of an event unrelated to a user. One or more binary digital signals may be generated to store a detection of one or more specified user circumstances in a log or memory at least partially in response to detection of the one or more specified circumstances.

The present teaching relates to managing online experiments. In one example, a plurality of experiment layers is created with respect to a plurality of online users. Each experiment layer includes at least one experiment each of which includes one or more buckets associated with respective features to be experimented on. Each of the plurality of online users is assigned to a corresponding bucket in each experiment layer, such that the user is simultaneously associated with multiple experiments in different layers. User event data related to the plurality of experiment layers are collected from the plurality of online users. One or more contaminated buckets are automatically detected based on the user event data.

An online system generates a custom audience based on user engagement with distributed content items. The online system monitors and stores user engagement with content items. A content provider submits to the online system a request to generate a new custom audience and selects audience parameters and user engagement types, such as users who watched a specified amount or percentage of a video, users who interacted with an online system page associated with the content provider, or users who clicked through online system content to a page associated with the content provider. Responsive to the request, the online system retrieves the corresponding user engagement data and applies the audience parameters to generate the custom audience and present it to the content provider.

For each of various sites, a mathematical representation is computed according to prescribed characteristics of users that have conducted one or more predetermined types of interaction with the site. Cluster of the sites are identified whose computed representations are similar according to prescribed criteria. Responsive to notification of an opportunity to deliver unidentified advertising to a given user via a given site, at least one ad is selected based upon factors including the ad having a prescribed performance history at one or more clusters containing the given site. The selected ad is transmitted to the given user at the given site, or a bid is transmitted for such placement of the ad.

An e-commerce method involves on-line viewing of a first article through a linking node for virtual merging on another structure. A particular application of the invention is directed to on-line apparel shopping involving a color matching scheme using color codes provided with images to be merged. For example, on-line viewing of one article, such as clothing, on another structure, includes creating an item from image-data corresponding to a colored article selected by an on-line viewer from an on-line viewer site with an image of a colored structure selected by the on-line viewer, and indicating whether the colored article and the colored structure satisfy a color-matching criterion.

An online system receives requests from content providers to present content to a target user of the online system. A threshold value for the target user in an auction is determined based on historical auction data associated with the target user and only content items with maximum bid values greater than or equal to the threshold value can win an auction to present the content item to the target user. A winning candidate content item and a bid value for the winning content item are determined. The online system calculates a winning bid value based on a function of a total bid value of the second place candidate content item, an organic bid value of the winning content item and the threshold value determined for the target user. The content provider of the winning content item is charged the larger of the threshold value or the winning bid value.

An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.