Systems and methods are provided for automatically generating and maintaining user profile cookie sets. The user profile cookie sets may be used by a web crawler when gathering data such as advertisement data associated with one or more websites. The cookie sets may be generated by choosing a user profile with a set of user traits, selecting a set of websites related to the user traits, and browsing the selected set of websites using a web crawler while allowing the website to place cookies in storage of the web crawler. The cookie sets may be maintained by selecting a website to browse, selecting a user profile associated with the selected website, loading a previously generated cookie set for the selected user profile into the storage of a web crawler, and loading the webpage while allowing the website to place, update, or replace cookies in the storage of the web crawler.

Computer-implemented methods of providing personalized recommendations to a user of items available in an online system, and related systems. First-level features including context features are computed based upon context data. A first-level machine learning model is then evaluated using the first-level features to generate predictions of user behavior in relation to a plurality of individual items available via the online system. A list of proposed item recommendations is constructed based upon the predictions. Second-level features are computed based upon the context data and list features based upon the list of proposed item recommendations and the corresponding predictions generated by the first-level machine learning model. A second-level machine learning model is evaluated using the second-level features to generate a prediction of user behavior in relation to the list of proposed item recommendations. A personalized list of item recommendations is provided based upon the prediction generated by the second-level machine learning model.

Disclosed is a system and method to collect data from a plurality of data sources to quantify the sentiment of users and predict objective outcomes. The method includes the step of collecting sentiment data from the first data sources associated with the user to determine an opinion of the user pertaining to products, and services through a sentiment analysis module. The method includes the step of collecting action data from the second data sources associated with the user to determine the behavior of the user through a behavioral analysis module. The second data sources include social media platforms, digital shopping platforms, and native applications. The method includes the step of collecting demographic data of the user from the third data sources to determine the profile of the user through a demographic profiling module. The third data source comprising a telecom server. The method includes the step of storing and analyzing data pertaining to the determined opinion of the user, determined behavior of the user, and determined profile of the user and computing a user profile value through a server. The method includes the step of presenting a persistent view of the analyzed data corresponding to the user through a user interface connected to a central computing device that configures the server with the telecom server.

An online system evaluates the quality of a content publisher displaying sponsored content items. To determine a likelihood of conversion actions associated with the sponsored content items, the online system uses information about users and their interactions with sponsored content items featured within the content publisher against interactions with sponsored content items featured within a benchmark system (e.g., online system). By determining a ratio of these interactions, the online system can determine a likelihood of conversion actions for the content publisher. The online system uses this likelihood of conversions to determine a publisher quality score that it uses to normalize third party value contributions toward placing sponsored content items on the content publisher. Thus, third party systems no longer need to be concerned about the intrinsic value of a given content publisher as third party value contributions are normalized based on the content publisher's conversion rates.

This application relates generally to automated systems and methods to identify an audience set for a marketing campaign period. In an embodiment, a system includes at least one processor operatively coupled with a datastore, the at least one processor configured to receive, from a user device, a request identifying a time period and an item class. The at least one processor is further configured to retrieve, from the datastore, user identifiers based on the time period and the item class. The at least one processor is further configured to determine a conversion value for each of the user identifiers by applying a statistical model to historical transaction data associated with the user identifiers. The at least one processor is further configured to determine an audience set comprising a subset of the user identifiers with the conversion value exceeding a threshold value.

Aspects of the subject disclosure may include, for example, a method, operating at a processing system including a processor, can include receiving a reply message from a communication device responsive to a query signal and including audio information collected at a presentation area, determining consumer presence information at the presentation area according to the audio information collected at the presentation area, determining a probability of a consumer advertising experience according to the consumer presence information, selecting first advertising media from a set of advertising media according to the probability of consumer advertising engagement, and presenting the first advertising media to the presentation area via the presentation system. Other embodiments are disclosed.

The present disclosure is directed toward systems and methods for identifying offers in networking system post. For example, systems and methods described herein identify one or more offer indicators in a networking system post and calculate a confidence score representing a level of confidence that the unstructured networking system post includes a merchant offer. In response to calculating a confidence score above a threshold value, systems and methods described herein prompt the composer of the unstructured post to convert the post into a structured offer. Upon converting the unstructured post into a structured offer, systems and methods described herein intelligently distribute the structured offer for use by networking system users.

A system that analyzes a user's communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

Transaction-enabled systems and methods for identifying and acquiring machine resources on a forward resource market are disclosed. An example system may include a controller having a resource requirement circuit to determine an amount of a resource required for a machine to service a task requirement, a forward resource market circuit to access a forward resource market, a resource market circuit to access a resource market, and a resource distribution circuit to execute a transaction of the resource on at least one of the resource market or the forward resource market in response to the determined amount of the resource required.

Disclosed herein are systems, methods, and devices for generating efficacy metrics. Systems may include a data object aggregator configured to receive a plurality of advertisement data objects characterizing online advertising content associated with at least one online advertisement campaign. Systems may also include an efficacy metric generator configured to generate a plurality of efficacy metrics characterizing an estimate of a probability of at least some of the plurality of advertisement data objects interacting with a target audience, where the generating is based on one or more properties of the plurality of advertisement data objects. Systems may further include a report generator configured to generate at least one report based on the plurality of efficacy metrics.