Abstract: There may be large numbers of content items available at current content provider systems. Incentive items may be inserted among content items, for example, associated with content categories, that may reward uses for viewing content items. When incentive items are selected by a user, the user may be associated with credit towards a bonus item or award a bonus item. Incentive items may have associated preference factors that may be manipulated to increase the probability of such incentive items being presented to the user.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating content-item recommendations that are based, at least in part, on data that is unique to respective geographical regions associated with respective users. For instance, the content-item recommendations may be based at least in part on sales data for the respective geographical regions.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating personalized game-notification feeds for users. In some instances, a remote computing system that offers one or more games for acquisition may determine which notifications generated by respective game publishers are likely to be of interest to different users and, after doing so, may generate personalized game-notification feeds comprising the selected notifications. Further, each of the users may provide feedback regarding one or more of the notifications in the notification feed, which the system may use to re-select notifications and re-generating personalized game-notification feeds.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating personalized game-notification feeds for users. In some instances, a remote computing system that offers one or more games for acquisition may determine which notifications generated by respective game publishers are likely to be of interest to different users and, after doing so, may generate personalized game-notification feeds comprising the selected notifications. Further, each of the users may provide feedback regarding one or more of the notifications in the notification feed, which the system may use to re-select notifications and re-generating personalized game-notification feeds.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.