A data enrichment system for enriching requests for advertising opportunities. The data enrichment system is configured to aggregate and index data to provide end user insights to marketers based upon information supplied by publishers in regards to advertising opportunities passed along in requests. The system is configured to take attributes passed along in a request associated with the advertising opportunity and provide enriched data based upon the attributes received in the request. The attributes can then identify corresponding enriched data that can be passed along to the marketers.

Described herein is a system and method for providing a conversation session with an artificial intelligence entity that is associated with a business entity. In some aspects, input is provided to an artificial intelligence entity advertisement system. The input is analyzed to determine the subject matter of the input. An artificial intelligence entity associated with the subject matter is then selected and provided to the user. The artificial intelligence entity recommends products or services that are provided by the business entity 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.

Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.

According to an implementation, a method for digital information retrieval in a social media platform includes transmitting, over a network, information to render a timeline of social content for a user of a client application. The timeline of social content includes messages posted on the messaging platform by user accounts that are connected to a user account of the user in a connection graph. The method includes computing, using a machine-learning algorithm inputted with relevance signals, a relevance level between the user account of the user and a user account not linked to the user account of the user in the connection graph, and transmitting information about a profile of the user to a computing device associated with the user account not linked to the user account of the user in response to the relevance level being greater than a threshold level.

A processor obtains first article information related to a first article which is owned by a first user and which is placed in front of or behind a transparent display, obtains, based on the first article information, an advertisement related to a product or a service which has been used by a second user who owns a second article related to the first article and who is different from the first user, and informs the first user of the advertisement.

A processor collecting advertisement (ad) events of a user using a mobile device, such as a mobile phone or eyewear, parsing the ad events from the mobile device, and generating an ad receptivity profile on the granularity of a user identification (ID) and an hour of day. In one example, the processor computes the percentage of ad time watched by the individual user, such as on an hourly basis, and by monitoring a click-through rate (CTR) of the respective user as a measure for user ad receptivity. The processor adjusts an ad allocation/ad load on a per user basis according the user level ad receptivity profile, resulting in dynamically providing ads on the mobile device display when a user is active and receptive viewing the ads.

A processor having a performance engine tracking user engagement of advertisement (ad) events using a mobile device, such as a mobile phone or eyewear, to generate a user level ad receptivity profile. In one example, the processor tracks both the percentage of ad time watched by the individual user, such as on an hourly basis, and ad engagement such as by monitoring a click-through rate (CTR) of the respective user as a measure for user ad receptivity. The processor downloads data from the performance engine to a server processor, and the server processor adjusts an ad allocation/ad load on a per user basis according the user level ad receptivity profile. The server processor dynamically provides ads on the mobile device display when a user is active and receptive viewing the ads.

A method, a computer program product and an apparatus for advertising to a crowd. The method comprises obtaining an estimated aggregated demographic data of an estimated audience of a video configured to be provided to the plurality of members, and performing a crowd-matching between the estimated audience of the video and a campaign to determine a matched campaign. The crowd-matching is performed by an auction between a plurality of participating advertisers, each of which providing a bid for presenting a campaign within the video that defines profile-based compensations. The matched campaign is provided to an audience of the video based on the estimated aggregated demographic data and without performing personalized matching of the matched campaign to a specific member.

The systems and methods 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 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, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.