Provided are methods and apparatus for scheduling automated information delivery to a user device. In examples, provided are computer-implemented methods for scheduling automated information delivery to a user device. At least a portion of the methods can be performed by a computing device including a processor. The methods can include (i) receiving, automatically via an application programming interface executed by the processor, information describing characteristics of potential hotel guests, characteristics of a hotel, and user input describing characteristics of an advertising campaign, (ii) creating, using a machine learning algorithm, advertising campaign information including instructions configured to direct an Internet search website to automatically display advertisements for the hotel that accompany search results, and (iii) sending the instructions to a server device configured automatically cause the advertisements for the hotel to be displayed on the user device. Various other methods, systems, and computer-readable media are also disclosed.

A digital experience enhancement system includes an ensemble deep learning model that includes an estimator ensemble and a neural network. The ensemble deep learning model is trained to generate a digital experience enhancement recommendation from an enhancement request. The ensemble deep learning model receives the enhancement request, which is input to the estimator ensemble. The estimator ensemble uses various different machine learning systems to generate estimator output values. The neural network uses the estimator output values from the estimator ensemble to generate a digital experience enhancement recommendation. The digital experience generation system then uses this digital experience enhancement recommendation to enhance the digital experience.

The present system provides users engaging with sharing platforms a method to monetize their contribution in generating ad revenue for various advertisers. The system can quantify the user contribution based on the user's behavior and action related to individual ads on the sharing platforms. The system provides a method of monetizing a user's contribution to ad revenue, thereby incentivizing users to share the selected ad to the user's audience.

Systems and methods are provided herein for determining personalized timing for generating for display advertisements to users. Rather than an expert determining time segments of a media asset most suitable for presenting advertisements to users, the most suitable time segments in a media asset for presenting advertisements to users may be customized based on a user's profile information and/or the user's level of engagement in a media asset. The media guidance application may parse a media asset into multiple time segments and determine one or more time segments associated with metadata that matches content characteristics preferred by the user. One or more advertisements may be presented to the user in these time segments determined by the media guidance application instead of the time segments determined by the expert.

Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.

Integrated systems and method for integrated management of advertising campaigns are provided. The system provides a user device with access to a centralized transaction platform server based on authentication information provided by the user device, which is verified by one or more integrated third party systems utilized by the one or more users. The system receives data regarding inventory from the one or more integrated third party systems, wherein the data is received via an application programming interface (API). The system aggregates a collection of available inventory based on the data regarding the inventory from the one or more integrated third party system. The system filters the aggregated collection of available inventory based on one or more categories of the inventory. The system generates, on a graphical user interface, a custom display based on a received criteria that matches the one or more categories of the inventory.

Content relevant to an operator of a music player may be identified using contextual information from music and music player environmental factors. Identification information for a song and music player identification information, environmental information, and listener information may be received. A song may be matched with information in a song categorization database so that one or more categories associated with the song are identified. A listener profile may be matched using a listener knowledge database to identify one or more categories associated with the listener. Content may be selected by matching the one or more song categories with categories in a campaign database and by matching the one or more listener categories with categories in the campaign database. Content includes, but is not limited to, advertising, trivia, referential information, weather, stock market prices, traffic information, news information, social media information, etc. The selected content is sent to the music player.

An example method includes receiving, from a user device, a first challenge content for inclusion in a challenge list and identifying, based on the challenge content and selection criteria, additional challenge content suggested for inclusion in the challenge list. The method further includes displaying the additional challenge content at a user interface of the user device and updating the selection criteria based on whether the additional challenge content is selected for inclusion in the challenge list.

A social networking system predicts a life event (e.g., birthday, change in marital status, relationship status, etc.) for a target user based on information associated with the user. The social networking system identifies gift suggestions to provide to one or more friends of the user based on the predicted event. A gift suggestion may include an invitation to purchase or send an item, voucher, or other gift to the target user, wherein the gift may be determined based on information about the target user’s interests obtained by the social networking system. The social networking system sends a gift suggestion to one or more friends of the target user, where the gift suggestion identifies the predicted life event and provides the gift suggestion to the target user’s friend. The advertisement may require action by multiple of the target user’s friends before the gift is sent to the target user.

Embodiments of system are disclosed in which selection strategies for a direct marketing campaign that identify consumers from a credit bureau or other consumer database can be planned, tested, and/or refined on a stable subset of the credit database. In some embodiments, once refined, consumer selection criteria may be used to execute the direct marketing campaign on the full consumer/credit database, which is preferably updated approximately twice weekly. In one preferred embodiment, the data for the test database represents a random sampling of approximately 10% of the full database and the sampling is regenerated approximately weekly in order to provide a stable set of data on which campaign developers may test their campaign. For each consumer in the sampling, the environment may allow a client to access and use both attributes calculated by the credit bureau and proprietary attributes and data owned by the client. The system allows for a plurality of clients to use the system substantially simultaneously while protecting the privacy and integrity of the client's proprietary data and results.