Abstract: The present disclosure generally relates to systems and methods for intelligently generating and assigning data objects to users. More particularly, the present disclosure relates to systems and methods for executing machine learning models to generate sets of candidate data objects for potentially assigning to users. A final data object is selected from the set of candidate data objects for each user in a manner that maximizes a target objective and satisfies a global constraint.

Abstract: An article of manufacture for predicting the popularity of media may include at least one processor readable storage medium and instructions stored on the at least one medium. The instructions may be configured to be readable from the at least one medium by at least one processor and may thereby cause the at least one processor to operate so as to determine a publication time associated with digital media, set a predetermined time threshold based on the publication time, analyze the digital media to determine at least one feature associated with the digital media, compile the determined at least one feature, predict a popularity associated with the digital media based on the compiled at least one feature, and output the predicted popularity. The at least one feature associated with the digital media may be determined before the predetermined time threshold.

Abstract: Techniques for the prediction of long-term popularity of digital media are disclosed. In accordance with some embodiments, a digital media prediction system may comprise a memory storing instructions and a processor configured to execute the instructions. The instructions may include obtaining digital media from at least one digital media content source, obtaining user activity data associated with the digital media from at least one client device, and determining at least one characteristic associated with the digital media. The instructions may further include updating a prediction model using the obtained user activity data and the determined at least one characteristic, determining a long-term popularity track by executing the prediction model, comparing the long-term popularity track to a predetermined threshold, and determining that the long-term popularity track exceeds the predetermined threshold. The long-term popularity track may be displayed on a graphical user interface.

Abstract: An article of manufacture for predicting the popularity of media may include at least one processor readable storage medium and instructions stored on the at least one medium. The instructions may be configured to be readable from the at least one medium by at least one processor and may thereby cause the at least one processor to operate so as to determine a publication time associated with digital media, set a predetermined time threshold based on the publication time, analyze the digital media to determine at least one feature associated with the digital media, compile the determined at least one feature, predict a popularity associated with the digital media based on the compiled at least one feature, and output the predicted popularity. The at least one feature associated with the digital media may be determined before the predetermined time threshold.

Abstract: High definition images are utilized in marketing, gaming, retail, e-commerce, multi-channel retail, mobile, tablet and/or social applications. A system displays the high definition image in its entirety, and in various states wherein portions of the high definition image are magnified. The various states show details of the portions of the high definition image that were not previously apparent. The system allows users to look for and detect certain target elements in the high definition image. These target elements may be used for gaming and/or marketing purposes.

Abstract: Systems and methods for making demographic predictions for websites and web-pages. Embodiments include a system and a method of making demographic predictions for websites. The system and method select one or more websites with known demographic attributes for use as training websites, obtain demographic attributes data of the training websites, determine first features of web-pages of the training websites and develop a prediction model using the determined first features and the obtained demographic attributes data. The prediction model predicts one or more values for a target demographic attribute. The system and method determine second features of web-pages of a target website and apply the prediction model to the determined second features of the target website to predict one or more values for the target demographic attribute of the target website.

Abstract: Systems and methods for making demographic predictions for websites and web-pages. Embodiments include a system and a method of making demographic predictions for websites. The system and method select one or more websites with known demographic attributes for use as training websites, obtain demographic attributes data of the training websites, determine first features of web-pages of the training websites and develop a prediction model using the determined first features and the obtained demographic attributes data. The prediction model predicts one or more values for a target demographic attribute. The system and method determine second features of web-pages of a target website and apply the prediction model to the determined second features of the target website to predict one or more values for the target demographic attribute of the target website.

Abstract: Systems and methods for predicting characteristics of a web user, determining a combination of websites to obtain a target demographic mix, determining a set of keywords to buy to obtain a target demographic mix, selecting websites from market research and designing websites to appeal to an audience with desired demographic characteristics. Systems and methods may include determining features of web-pages of ad-carrying, target websites, applying prediction models to the determined features of the ad-carrying, target websites to predict values of demographic attributes of the ad-carrying, target websites, receiving one or more inputs including a target demographic mix, receiving a number that indicates an amount of visitors of the ad-carrying, target websites, and determining a combination of websites that provide target demographic mix based on the predicted values of the demographic attributes and number of visitors of the ad-carrying, target websites.