Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for stochastic multi-period multi-objective optimization based recommendation system. An embodiment assigns a respective plurality of stochastic parameters to a plurality of recommendation objectives. The embodiment further associates each program of a plurality of programs with one or more recommendation objectives of the plurality of recommendation objectives, and selects, during a first recommendation time period, a first set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters. The embodiment then generates a first ordered list of recommended programs from the plurality of programs based on the first set of operative recommendation objectives.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining an optimal supplemental content for a media stream menu interface to maximize the consumption of the media stream content by users. An example embodiment operates by performing automated content recognition (ACR) on the media stream, thereby determining optimal supplemental content. The embodiment identifies a plurality of potential supplemental content items in the media stream based on the characteristics of the media stream. The embodiment then outputs the optimal supplemental content to a plurality of predetermined media devices.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for pairwise comparison rating to reduce presentation bias in content recommendation. An embodiment operates by generating respective ranking values for a plurality of content items based on interactions between user devices and the content items. The respective ranking value for each content item is adjusted based on additional interactions between the user devices and the content items compared to predicted interactions between the user devices and content items. When a first user device of the plurality of user devices requests content, pairwise distances between the respective ranking values for the content items and respective weighted values for historical content items that have been previously interacted with by the first user device are determined.

Abstract: Disclosed herein are computing system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations and sub-combinations thereof, for generating customized/personalized content browser UIs and browsing experiences. For example, a computing system may be configured to obtain data about one or more user interactions with a content browser user interface (UI). In some cases, the content browser UI displays a plurality of groups of tiles representing different content items. Additionally, the computing system may be configured to identify, based on the data about the one or more user interactions, a first template specifying a layout of the content browser UI and a second template specifying a configuration of one or more groups of tiles of the plurality of groups of tiles. Further, the computing system may be configured to update the content browser UI based on the second template.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content to a user. An embodiment identifies a first set of content items based at least on a first set of weights respectively associated with different user interests, causes the first set of content items to be presented to the user, determines a measure of user interaction with the first set of content items, provides the measure of user interaction to one of a multi-arm bandit (MAB), contextual MAB, or reinforcement learning model that selects, based at least on the state information and the measure of user interaction, a second set of weights respectively associated with the different user interests, identifies a second set of content items based at least on the second set of weights, and causes the second set of content items to be presented to the user.