Example methods and apparatus disclosed herein detect advertisements embedded in online media. An example apparatus includes memory, computer readable instructions, and processor circuitry to execute the computer readable instructions to render a graphical representation of progress associated with presentation of primary media distributed by an online media provider to a consumer device, the primary media including an event notification, the graphical representation to have a length and to depict the event notification at a location on the graphical representation. The example apparatus also includes processor circuitry to execute the computer readable instructions to determine a first time associated with the event notification based on the length of the graphical representation and the location of the event notification and determine a second time associated with a media feature of the primary media.

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.

The present disclosure relates to systems and methods for identifying products and monitoring planogram compliance using analysis of image data. In one implementation, the method may include accessing at least one planogram describing a desired placement of a plurality of product types on shelves of a plurality of retail stores; receiving image data from the plurality of retail stores; analyzing the image data to determine an actual placement of the plurality of product types on the shelves of the plurality of retail stores; determining at least one characteristic of planogram compliance based on detected differences between the at least one planogram and the actual placement of the plurality of product types on the shelves of the plurality of retail stores; and receiving checkout data from the plurality of retail stores reflecting sales of at least one product type from the plurality of product types.

Embodiments of the present disclosure relate to a immersive digital advertising system and method thereof for providing one or more local merchants the ability to expand their advertising outreach to local and nearby consumers via digitally displayed advertisements, the system having an electric pod cargo trailer and an electric tow bicycle coupled to the electric pod cargo trailer for hauling the electric pod cargo trailer along a predetermined Ad pedestrian route.

A method includes receiving client data of a client that includes at least one of clickstream data and analytic data of the client. For each of a number of trained machine learning (ML) models corresponding, respectively, to a number of campaigns, campaign-specific features are extracted from the client data, and a campaign interest prediction score is generated by inputting the campaign-specific features extracted for the ML model into the ML model. At least one campaign, from among the plurality of campaigns, is assigned to the client based on the generated campaign interest prediction scores. The clickstream data includes a plurality of pages visited by the client, and the analytic data of the client includes at least one of phone call data, chat message data, email data, or survey data of the client.

Generating a cannibalism score for a paid ad in a search engine results page (SERP) by gathering keywords relevant to an advertiser, defining rules that compute a cannibalism score for the ad in relation to a corresponding unpaid listing, where the cannibalism score estimates the reduction in revenue to the advertiser due to the ad appearing in the same SERP as the corresponding listing, providing a keyword to a search engine, receiving a SERP from the search engine, determining the position of a first ad placed by the advertiser from among one or more ads in the SERP, determining the position of a corresponding unpaid listing from among a plurality of unpaid listings in the SERP, and applying the rules to the ad and to the corresponding unpaid listing to obtain a cannibalism score for the ad.

An information processing method, a server, and a computer storage medium are provided. The method includes: generating a first information identification code according to first information, the first information representing an information source; providing the first information identification code to a terminal; receiving a first request from the terminal and allowing a terminal user of the terminal to follow an information service, the first request carrying the first information; and recording the first information and tracking and obtaining conversion information of the information source according to the first information.

Provided are a viral marketing service providing system for distributing advertisement costs for each marketer by analyzing a behavior of a content receiver, and a method therefor. For the viral marketing service for distributing advertisement costs for each marketer, the system comprises a content receiver behavior analyzing unit and a traffic quality index calculating unit, thereby precisely filtering abusing marketers (abusers), and thus can minimize damages to legitimate marketers with good intentions at the time of distributing predetermined advertisement costs. Also, the system can extract, by using a traffic quality index, high quality marketers who generate active behaviors on an advertiser's landing page or start a rumor of contents in a manner which aids in the advertising purpose of an advertiser, thereby distributing some of the advertisement costs paid by an advertiser to the marketers at a higher rate than other marketers.

An analytics system jointly predicts values for multiple unobserved individual-level features using aggregate data for those features. Given a dataset, a transformation is applied to individual-level information for the dataset to generate transformed data in a higher dimensional space. Bag-wise mean embeddings are generated using the transformed data. The bag-wise mean embeddings and aggregate data for unobserved individual-level features for the dataset are used to train a model to jointly predict values for the unobserved individual-features for data instances. In particular, a given data instance can be transformed to a representation in a higher dimensional space. Given this representation, the trained model predicts values for the unobserved individual-level features for the data instance, and the data instance can be augmented with the predicted values.

The present disclosure relates to a system and method for use with video and online gaming. Embodiments may include providing, using at least one processor, a video game or online game and integrating an application programming interface with the video game or online game. Embodiments may include displaying an advertisement on a three dimensional (“3D”) or four dimensional (“4D”) object associated with the video game or online game.