A method is described of facilitating targeted content delivery in a network comprising a service provider network (SPN) and a user profile data database external to the SPN. The method comprises: receiving, from a demand side platform, segment definition data comprising a list of user attributes and corresponding user attribute values associated with a targeted content delivery process; and communicating, to a server, the segment definition data, wherein the server has access to the database, to identify one or more candidate users to be targeted based on matching the user attribute values with user attribute values in the database. The method also comprises receiving, from the server, a list of identifiers, wherein each identifier is usable to identify one or more candidate users to be targeted; and communicating the list of identifiers to an entity in the SPN which is responsible for facilitating the delivery of targeted content.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

An online system receives information describing a target group of online system users from a third party system that includes one or more user properties, which may identify an action performed by a an online user within a time interval to include the user in the target group. When an opportunity to present advertisement requests to a user is identified, the online system retrieves actions associated with the user and times associated with the actions. If an action associated with the user matches the action identified by the user properties and occurred within the time interval identified by the user properties, and other characteristics of the user satisfy at least a threshold number of the user properties, the online system includes the user in the target group. Advertisement requests having targeting criteria, including the target group, satisfied by characteristics of the user are evaluated for presentation to the user.

Methods and systems are provided herein for recommending a media asset based on a geographic location at which that media asset was frequently consumed. For example, the system may monitor a location, such as New York City or Times Square, to determine popular media assets watched there, such as “The Avengers,” and when another user visits New York City, the system may then notify the user that the movie, such as “The Avengers,” is associated with New York City. The system stores the geographic locations associated with the media asset in the database based on the consumption of the media asset so that other users may be notified which media assets are associated with each geographic location.

A method is disclosed in which one or more pieces of user information are obtained. The one or more pieces of user information are indicative of at least one attribute of a user and/or include at least one piece of information associated with the user. In the method, one or more pieces of user probability information are determined based on the one or more pieces of user information. The one or more pieces of user probability information are indicative of a probability that the one or more pieces of user information are linked to the user. One user identity is determined based on the one or more pieces of user probability information. It is further disclosed an according apparatus, computer program and system.

A method and system for recommending online content based upon the predictive value of reviews provided by third party reviewers correlated to the identity of those reviewers. The correlated data is used to predict how a target user would rate an unrated item, and to make recommendations to the target user.

The disclosure relates to securing and enabling user control of profiling data, blockchain-driven matching of users and advertiser-identified anonymous profiling data records of interest, and smart contracts encoded by blockchain for executing transactions. The system may include an anonymized database of profiling data, which is unlinked to any user. The system may implement a private blockchain to store user-defined settings that provide user control over whether and how the profiling data may be used. If a grant to use the data is received, a link is stored that allows the system to identify a user associated with the anonymous profiling data records. If the grant is revoked, the link may be removed. The system may also implement public blockchain technology to record a public information relating to grants, online marketing transactions, making them verifiable, immutable, and transparent for various stakeholders including advertisers, publishers, and users.

A computer-implemented method, computer program product, and computing system for receiving a contribution for a social platform from a client; funding the social platform; and calculating a contribution ROI experienced by the client as a result of the contribution to the social platform.

Systems and methods are provided for determining a quantity of network location visitors that are likely generated or encouraged by specific offline events. A corresponding number of leads may then be attributed to and associated with those specific events. Ongoing conversion activity of those visitors may be tracked and associated with the offline events. Conversions of those visitors may be attributed entirely or partially to one or more specific offline events. The effectiveness of each offline may then be evaluated based on aggregate lead and conversion information.

Systems and methods are provided for dynamically generating a product description personalized for a target user account. Product description data and/or product comment data associated with the product are obtained from a server via a communication network. Based on the product description data and/or product comment data, a product description template, comprising fields to be populated, is generated and stored in memory. Comment data associated with a product of interest is obtained from a server via the network. The comment data originates from a set of user accounts each having a similarity score with a target user account that is above a similarity threshold. Based on the set of comment data, comment-based description data are generated and stored in memory. Based on the product description template and the comment-based description data, a product description is generated for presentation for the target user account.