Systems and methods are provided for discovering advertisements on publisher web pages and for determining whether the advertisements are obscured by other content on the web pages. An advertisement tracking and discovery system may use web crawler applications to explore multiple publisher websites. The web crawler applications may gather advertisement data that includes information associated with detected advertisements, placement pathways by which the advertisements were placed on the publisher web pages, and obscuration information for the detected advertisements. Obscured advertisements that have been detected may be ignored during advertisement tracking and discovery operations or obscuration information may be stored and provided with other advertisement information for the obscured advertisements. The advertisement data may be aggregated and formatted to be provided to a user. The provided data may include advertisement screenshots and web page screenshots containing the advertisements.

A computer-implemented method and system for advertising that performs the steps of delivering an electronic advertisement comprising one or more menu options and a reference to a network location for retrieving specified content associated with each menu option for inclusion in a first electronic document, receiving a selection of one or more menu options from the electronic advertisement and delivering a subsequent accessible document including content from the referenced network location associated with the menu option selected, the subsequent accessible document including the electronic advertisement.

Provided are methods, systems, and media for Internet Advertising. Exemplary methods may include: providing an advertisement including a first identifier to a website using at least one of a template and an application programming interface (API), the advertisement to be displayed on the website; receiving a communications session initiated by an end customer using the first identifier, the communications session including a second identifier associated with the end customer; accepting the communications session when the second identifier is not included in a black list; retrieving a record associated with the end customer using the second identifier; determining to provide a promotional message to the end customer using the record; selecting an offer using the record; providing the promotional message to the end customer using the communications session, the promotional message including the offer and a request for an indication of interest.

Methods, systems, and programs are provided to determine event-level traffic quality for event(s) related to user interaction with online content (e.g., via a webpage, a mobile application, etc.). Data related to a current user event and past user events may be received, where such data may include information regarding a set of entities associated with each respective user event. A feature value set for the current user event is generated based on the information regarding the respective sets of entities associated with the current user event and the past user events. Based at least on such feature value set, a traffic quality score for the current user event may be determined, e.g., based on a weighted combination of elements of the feature value set. An entity-level traffic quality score for an entity may be determined based on event-level traffic quality scores of user events that involve that entity.

Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.

Embodiments disclosed provide new approaches for determining fractional attribution using aggregate advertising information. A channel weighting approach may derive the causal influence weight of any channel on conversions. In some embodiments, the approach may include arranging the conversion rate of each channel into different funnel stages, constructing aggregate-level data, and running a multi-stage regression computation using instrumental variables. This approach works with any number of different types of advertising channels, including online and offline channels, and provides the most accurate credit to each channel or sub-channel involved.

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (DBMLII) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A user interface configuration request associated with a dataset comprising a set of features is obtained. A set of top features associated with the dataset that are most likely to be useful for machine learning classification is determined. A feature user interface configuration associated with each top feature in the set of top features is added to an overall machine learning guided user interface configuration. The overall machine learning guided user interface configuration is provided for a user.

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (DBMLII) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A double blind machine learning request is obtained. A third party's shared dataset and corresponding external predictions data determined by the third party based on an unavailable dataset is determined. Proprietary data corresponding to the shared dataset is determined. A dataframe comprising at least subsets of the determined shared dataset, external predictions data, and proprietary data is generated. A set of top features from the dataframe is determined. Top features data is utilized to generate a machine learning structure. The generated machine learning structure is utilized to produce machine learning results. The machine learning results are translated into commands and provided to the third party.

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (DBMLII) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A dataset comprising a set of features is obtained. Contents of the dataset are partitioned into a features dataframe and a labels dataframe. Features data in the features dataframe is encoded. A score for each feature in the features dataframe is calculated. Top features in the features dataframe are determined based on the calculated scores. The determined top features are provided to a machine learning structure generator.

The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (DBMLII) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.