This application relates to apparatus and methods for providing a unified serving platform that allows for the reusability of machine learning models across a plurality of websites to determine personalized content. For example, a computing device trains a machine learning model with session data identifying browsing events and transaction data identifying purchasing events for a plurality of users. The computing device receives and stores session data and transaction data associated with a first website for the customer. The computing device may then receive a request for content to display to the customer on a second website. The computing device generates label data based on the session data and transaction data associated with the first website, and executes the trained machine learning model with the label data. Based on execution of the trained machine learning model, the computing device generates content to display on the second website, and transmits the content.

In an embodiment, the disclosed technologies include determining a digital identifier, computing, using aggregate digital event data obtained from at least one computing device, digital feature data relating to the digital identifier, inputting the digital feature data relating to the digital identifier into a digital model that has machine-learned correlations between digital feature data and digital propensity prediction values, receiving, from the digital model, a predicted propensity value associated with the digital identifier, determining a propensity score based on the predicted propensity value, causing a digital content item to be displayed on a user interface of a network-based software application associated with the digital identifier if the propensity score satisfies a propensity criterion.

Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

Disclosed herein are systems and methods for determining electric pulses to provide to an unattended machine based on remotely-configured options. An example method includes: detecting presence of the unattended machine in proximity to the mobile device. After detecting the presence, the method includes: receiving, from a server, information about a first set of remotely-configured options. In response to receiving the information about the first set of remotely-configured options, the method includes: displaying user interface objects that allow for selection of respective options in the first set of remotely-configured options. After detecting a selection of a first user interface object, receiving, from the server, specifications regarding electric pulses to be provided to the unattended machine by a pulse-providing device. After sending an authorization grant and the specifications to the pulse-providing device, the method includes: receiving an indication that the electric pulses were provided to the unattended machine according to the specifications.

A system provides advertising by using a device discovery process to automatically determine an information about a home network system of a user. When it is determined that a first advertisement has been caused to be presented via a first content providing service or a first media access device, the information is used to automatically prevent a second content providing service or a second media access device from causing a second advertisement to be presented.

Aspects of the subject disclosure may include, for example, obtaining a first mapping of first target sentiments associated with a first content item, obtaining actual sentiments of a first user during a presentation of the first content item to the first user, comparing the actual sentiments of the first user to the first target sentiments to generate a comparison result, storing the comparison result in a first profile for the first user, and providing the first profile to a creator of the first content item. Other embodiments are disclosed.

Techniques described herein are directed to e-commerce tags in multimedia content. In an example, multimedia content is received and item-recognition techniques are performed to identify one or more items referenced in the content. An interactive element is generated that indicates information about a given referenced item, and that interactive element is displayed while the multimedia content is output. Selection of the interactive element may cause a purchasing user interface to be displayed with item and/or or payment information prepopulated based at least in part on identified attributes of the item and/or user preferences determined from historical purchase data associated with the user.

Providing a state of desired effects (SDES) simultaneously on one or more presentation devices using a system having one or more switchers, each switcher having a unique identification code, activation parameters that control when the switcher is operable to send activation signals, and one or more triggers. Each trigger has a trigger identification code associated with a set of one or more presentation devices or associated with another switcher and another trigger, and triggering criteria relating to an occurrence of a particular event. One method includes activating a first switcher, receiving, at the activated first switcher, event information indicative of the occurrence of a particular event, determining if the event information meets triggering criteria for a trigger of the first switcher, and in response to the event information meeting triggering criteria for a trigger of the first switcher, activating the trigger of the first switcher that meets the triggering criteria.

An artificial intelligence (AI)-based content communications system leverages microtrends identified from conversations in real-time to support targeted content mapped to the identified microtrends. The communications system receives conversation information of participants, including text of conversation, timestamp, and, optionally, geographical information, from a listening service authorized to capture the conversation information; determines one or more topic microtrends having above-threshold activity; retrieves content with tags having generated keywords associated with a corresponding topic microtrend; and generates a message comprising the content and, optionally, a topic microtrend dashboard to provide to an identified contact associated with the topic microtrend. In some cases, the content is directly pushed to a social media handle associated with the content.

Systems, methods, and non-transitory computer-readable media can generate individual feature data for each user of a plurality of users. A first cohort comprising a first plurality of users is generated, wherein the first plurality of users are selected from the plurality of users based on the individual feature data. A first set of cohort feature data associated with the first cohort is generated based on individual feature data for the first plurality of users. The first set of cohort feature data and a first set of cohort membership information are transmitted to a modeler. The first set of cohort membership information identifies each user of the plurality of users.