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.

The system links Internet web page context with audience usage and location data to support advertising efficiency and effectiveness. An ontology of categories is created where domains and website pages are classified and scored against the links on those pages and the meta-tag key word pools that are harvested from those web pages. An ontology of high level categories are derived from the frequency of the key words appearing within the domain URL addresses of the pages, the domain of the links on those pages or within the content of the pages themselves. A method includes building a training set of web pages from a plurality of ad networks and sites where the system captures impressions in the form of real-time bids as well as click through events that include the IP address, the domain, the time of day and day of week, ad size and position, browser type, and bid amount whereby the training set is aggregated in a database whereby successful bids can be used in combination with audience and category attributes to model and score impression bids that combine the optimal mix of audience attributes, location, categorical affinity and bid price.

A promotion offering system and method is disclosed. The promotion offering system and method selects consumers for a test promotion that has one or more attributes, and is configured to generate test data for multiple consumer groupings. The promotion offering system and method may use the test data in analyzing whether to send a promotion to a consumer. For example, the promotion offering system and method may use one algorithm to generate a list of ranked promotions, and may use the test data in order to adjust the list of ranked promotions (such as replacing a highest ranked promotion with another promotion).

This specification describes technologies relating to content presentation. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving ad information from a seller; generating, using one or more processors, a single conversion ad using the received ad information, where the single conversion ad has an available inventory of one such that only a single conversion of the ad can be performed; transmitting the single conversion ad to one or more potential buyers; receive an input from one of the one or more potential buyers; and notifying the seller of the user input. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.

Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for multimedia playback. One of the methods includes: receiving, by a multimedia playback device from a multimedia server, a list indicating a plurality of multimedia files; caching, by the multimedia playback device, the plurality of multimedia files in a cache storage of the multimedia playback device; requesting, by the multimedia playback device from the multimedia server, an indication of a primary multimedia file to be played by the multimedia playback device; determining, by the multimedia playback device, a secondary multimedia file from the plurality of multimedia files to be played by the multimedia playback device; and playing, by the multimedia playback device, the secondary multimedia file when a playback condition of playing the secondary multimedia file is satisfied.

Devices, systems, and methods for allocating consumer communications can include obtaining consumer activity data, designating a number of consumer communication campaigns concerning consumer features based on the consumer activity data, entering consumer activity data as inputs to one machine learning model for each determined consumer communication campaign, each machine learning model configured determine a campaign consumer activation profile based on the entered consumer activity data, and assigning the consumer communication campaigns to consumers based on the determined campaign consumer activation profiles.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing a provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

A campaign management system manages campaign datasets, wherein a campaign dataset corresponds to selections and instructions for executing an online campaign. A campaign dataset might be created by a human or a machine, or multiple humans and computers in a crowd-sourcing approach. Campaign datasets can be processed to determine an expected quality metric, a post-run quality metric, and a confidence level. A campaign comparator can compare campaign datasets to identify similar campaign datasets so that an expected quality metric, a post-run quality metric, and/or a confidence level from one campaign dataset can be imputed to another campaign dataset.

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.

Some embodiments described herein relate to a computer-implemented method that includes accessing behavioral data, such as web visitation data, of multiple users. A sparse behavioral vector can be defined for each user based on the behavioral data. Each element of each sparse behavioral vector can represent a different potential detectable behavior such that each sparse behavioral vector encodes the behavioral data for that user. Multiple supervised learning models to each sparse behavioral vector to densify the vectors, defining multiple dense behavioral vectors. An unsupervised machine learning technique can be applied to the dense behavioral vectors to cluster, or define subpopulations, based on similarities between the dense behavioral vectors. Delivery of targeted content to a user can be facilitated based on a dense behavioral vector associated with that user being associated with one or more of the clusters or subpopulations.