A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.

A system and method is provided for delivering customized information related to a specific product of interest to consumer. In practice, the consumer uses a suitably enabled portable, mobile and/or wireless device (e.g., such as a mobile camera phone) to scan or otherwise read a marker associated with a product, retail item or other article of interest. From the marker, a unique ID is obtained (i.e., the marker ID). The marker ID is then used to cross-reference a URL or other like address in a database that relates marker IDs to corresponding URLs. The target URL is returned to the consumer's device and an http session is established with a content management server at the target URL. In one suitable embodiment, the content management server obtains a SKU and/or template web page ID that are associated with the marker ID in a database. Suitably, the SKU relates to the specific product with which the marker was associated. Having in this way determined the actual specific product of interest to the consumer, customized information related thereto can be delivered to the consumer's device, e.g., via a web page optimized for the identified device. Optionally, the web page content and/or template is obtained from a database that associates the same with the template web page ID received by the content management server.

The present disclosure provides a method of determining a user's long-term value to a wagering network and identifying new users similar to the users that provide long-term value to the wagering network. This method determines a user's long-term value and the user's engagement with a wagering network and places the users into cohorts. The method also provides finding correlations with the users' data and then correlating the data of new users and comparing the correlation coefficients of the new users with the older users to group the new users into the cohorts similar to the older users to predict their long-term value to the wagering network.

Systems, methods, and computer program products are provided for auctioning advertising inventory for various applications, including for mobile applications. In various embodiments, a request for an advertisement is received at a first server from a client device. The request is sent, from the first server, to two or more remote servers. A bid value and an asset to be presented at the client device is received from each of the two or more remote servers. A highest bid is determined from the received bid values. Whether the asset associated with the highest bid is compliant based on the request is determined. When the asset is determined to be compliant, the asset is sent to the client device.

A method for extracting a related disease from a patient prescription and providing extracted disease related information is provided. The method prioritizes a predefined primary disease name based on a medical department and a prescribed drug on a patient prescription, and additionally extracts one or more secondary disease names based on a predefined severity level thereof. In this connection, the disease related information is outputted based on a weight allocated thereto based on whether the extracted disease is the primary disease and based on the disease severity level thereof. Thus, when extracting the related disease based on the drug information on the patient prescription, the primary disease is prevented from being excluded from the output. Further, the disease-related information is output based on the predefined diseases severity level.

A system and method for online marketing, comprises reading, by an electronic device, a machine-readable code corresponding to an object of interest or interpreting one or more characteristics of the object of interest itself in order to identify it; generating a request to a designated computer server for content related to the object of interest in response to processing a decoded address on the machine-readable code or the value of the one or more characteristics of the object of interest; and outputting a link to the content or outputting the content itself to a device in response to a receipt of the request.

Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

The systems and methods described herein use a tag for collecting data associated with a content object of a content page. The tag may be a universal tag which can provide a single point of data collection and thereby reduce the number of network requests due to piggyback tags. The data collected from the content page may be communicated to a stream management system which can process the data and split the data into multiple data streams to be fed to a downstream system. The downstream system can further process the data and communicate to another downstream system. In some embodiments, the stream management system can access the repositories of its downstream system and veto the data flows created by the downstream system.

A server and a method for generating a user-advertisement (user-ad) matching list for online advertisement is provided. The method includes obtaining profile information and advertisement exposure history information of users exposed to a plurality of advertisements through an ad impression server, obtaining advertisement information of an advertisement to be delivered by an advertiser, identifying candidate users for the advertisement of the advertiser among the users, based on the profile information of the users, predicting an access time when the candidate users will access the ad impression server, selecting target users to be provided with the advertisement of the advertiser among the candidate users, based on the predicted access time, obtaining a preference of each target user for an advertisement product of the advertiser, and generating the user-ad matching list by matching the advertisement of the advertiser with the target users, based on the preference.

A system, method, device, and platform for generating targeted advertisements. Transaction information for a user is received. The transaction information associated with the user is verified. The information is reconciled with advertising data associated with the transaction information. Targeted advertisements are created based on the available advertising data. The targeted advertisements are communicated to the user.