A machine may be configured to manage user data in a user data store. For example, the machine identifies a rule associated with a campaign for serving online ads in a social networking service (SNS). The rule specifies a maximum number of user events associated with the online ads included in the campaign to occur during a time window, for a member of the SNS. The machine identifies a bucket that stores metadata pertaining to user events associated with the particular member that occurred during a time period that corresponds to the time window specified in the rule. The machine performs an analysis of the metadata pertaining to the user events associated with the particular member that occurred during the time window specified in the rule. The machine determines that, for the particular member, the rule is not violated based on the performing of the analysis of the metadata.

Embodiments include a platform and/or application configured to select an event on a first electronic site using a source device, and integrate with a presentation of the event a selector for a micropromotion application. An invitation to the event is automatically generated at the source device in response to selection of the selector. Invitees are added to the invitation from at least one of a social media application and a contact application coupled to the source device. The invitation is configured to include a reply link, and the reply link is configured to generate a reply to the source device. The reply link is configured to generate, via one or more third party platforms, presentation of service offers at the source device and invitee devices of the invitees. The service offers include offers of services of the event provider and services related to the event.

An apparatus, computer-readable medium, and computer-implemented method for determining effectiveness of a targeting model, including setting target variables corresponding to an initial group of consumers, the initial group of consumers corresponding to a subgroup of an experimental group of consumers which is larger than the initial group of consumers, applying the targeting model to an experimental set of consumer data corresponding to the experimental group of consumers to generate a plurality of experimental scores which score the experimental group of consumers according to projected fit with the target profile, identifying any experimental scores in the plurality of experimental scores which correspond to the initial group of consumers, and determining an effectiveness of the targeting model with respect to the target profile based at least in part on the target variables and one or more metrics which quantify the identified experimental scores relative to the plurality of experimental scores.

Methods and apparatuses for sorting seller listings or advertisements of a seller network. In one embodiment, a method includes: determining an indicator of potential revenue for a first party from price information of a list of entities, wherein revenue generated according to the price information of at least some of the list of entities is to be split among a plurality of parties; and, sorting the list of entities into a first list based at least partially on the indicator of potential revenue.

A multimedia commerce service processing method includes the steps of receiving a signal for selecting a first product provided by the multimedia commerce service, detecting body size information of a user from login information of an account of the user who uses the multimedia commerce service, generating a first avatar graphic image including the detected body size information of the user and transmitting a signal for outputting an image of the first product in a specific area of the first avatar graphic image to a display device. A multimedia commerce service processing apparatus includes a receiving module configured to sense a signal, a communication module configured to transmit and receive data, a storage module configured to store login information of an account of a user and a controller configured to control operation of the multimedia commerce service processing apparatus.

A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.

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.

Preparing a content package by determining a requesting user profile based on the requesting user identity. This includes calculating an inclusion value for a content file based on at least one factor. The factor may be a social networking factor. The social networking factor is based on a profile attribute linking the requesting user identity to at least one friend user identity and a content rating attribute linking the content file to the friend profile. Other factors are a content aging factor based on a content age value and a request length factor based on a content length value. The calculated inclusion value is compared to an inclusion condition. If the calculated inclusion value satisfies the inclusion condition, incorporating the content file into the content package.

Techniques for intelligently managing service requests using a service request outcome prediction and a dynamically determined probability threshold are disclosed. In one embodiment, a computer-implemented method is disclosed comprising receiving a request for service directed to an online service provider, determining a feature vector for the received service request, the feature vector determination comprising identifying information associated with the request and a response of the service provider, the feature vector being based on the identified information, analyzing the received request using a trained outcome prediction model and the feature vector, and determining a win probability based on the analysis, the win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider's response, making a request throttling determination based on the win probability and a threshold probability, and managing the service request based on the request throttling determination.

Systems and methods of providing media plans, such as media buys or media sells, across multiple media types by using a deep learning/natural language processing (artificial intelligence (AI) powered system and/or other machine learning models).