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 reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for real-time online learning for short-form content ranking. An example embodiment operates by receiving a user feedback event that includes user feedback on a short-form content item played using a media device, and publishing a message with the event to a publish-subscribe (pub/sub) topic. Retrieved with a pull operation, the user feedback event from the message is saved to a real-time data store. A recommendation engine is triggered to generate a playlist batch of short-form content recommendations based on the user feedback event. Short-form content items specified in the playlist batch are transmitted to the media device for playback. User feedback on short-form content items in an immediately preceding playlist batch during the same usage session can inform the selections made in the generated playlist batch.

Abstract: A coating system configured to be applied to a thermal barrier coating of an article includes an infiltration coating configured to be applied to the thermal barrier coating. The infiltration coating infiltrates at least some pores of the thermal barrier coating. The infiltration coating decomposes within at least some pores of the thermal barrier coating to coat a portion of the at least some pores of the thermal barrier coating. The infiltration coating reduces a porosity of the thermal barrier coating. The coating system also includes a reactive phase spray formulation coat configured to be applied to the thermal barrier coating.

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 reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for providing content to a user so as to balance known content of interest to the user, and potential new content of interest (e.g., exploration content). An example embodiment operates by receiving and analyzing behavioral data of a user as it relates to exploration content. This behavioral data may include the user selecting, slowing scrolling, pausing scrolling, or other actions that indicate interest in provided exploration content. Based on this data, the user's proclivity for exploration content is determined. This proclivity is compared to a current exploration value associated with the user, and used in one of a variety of different ways to calculate an adjustment to the user's exploration content value, which dictates an amount of exploration content that will be provided to the user.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a pseudo-user neural network model based a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy and an interest prompt based on an estimated state of the pseudo-user neural network model. The embodiment then generates, using the trained conversational recommendation system, a real-time recommendation having high play probability based on the minimal number of iterations of conversation between a user and the trained conversational recommendation system.

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 system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a recommendation for a media content of a first form of content based on user interactions with a second form of content. The first form of content is of a different length than the second form of content. An example embodiment operates by determining interaction based data associated with a second form of content based on a user interaction with a first media content. The interaction based data are provided to a machine learning model along with historical data indicative of a user behavior with media contents of the first form or the second form of contents, and metadata associated with the first media content. The machine learning model outputs a second media content of the first form.

Abstract: A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. A recommendation system can generate similarity scores for pairs of content items selected from a set of available content items based on metadata associated with the content items. The recommendation system can then generate a set of interest based clusters for the set of content items based on the similarity scores. The recommendation system can determine for a user a group of user interest clusters selected from the set of interest based clusters. Recommendation candidates for the user can be selected for the user from among content items in the group of user interest clusters and can be presented via a user interface.

Abstract: A set of content items can be accessed by a community of users having a set of interests. A set of interest based clusters for the set of content items correspond to the set of interests. For a user, a recommendation system can determine a group of user interest clusters selected from the set of interest based clusters. A popularity score for each content item of the set of content items with respect to the community of users can be generated, and an interest based popularity score for a content item within the interest based cluster can be generated based on a rank of the content item based on the popularity score of the content item. Recommendation candidates for the user can be generated based on the interest based popularity score of the content item for each content item in the group of user interest clusters.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for deep video understanding with large language models. An example embodiment operates by determining a relationship between respective first and second visual elements for each of a plurality of frames of a content item based on respective element types and respective locations for the respective first and second visual elements. For each of the plurality of frames, a respective visual prompt is generated describing the relationship between the respective first and second visual elements. Based on an audio-to-text conversion of audio content associated with the frame or classification of aural elements of the audio content, a respective audio prompt describing the audio content associated with each frame is generated.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

Abstract: A coating system configured to be applied to a thermal barrier coating of an article includes an infiltration coating configured to be applied to the thermal barrier coating. The infiltration coating infiltrates at least some pores of the thermal barrier coating. The infiltration coating decomposes within at least some pores of the thermal barrier coating to coat a portion of the at least some pores of the thermal barrier coating. The infiltration coating reduces a porosity of the thermal barrier coating. The coating system also includes a reactive phase spray formulation coat configured to be applied to the thermal barrier coating. The reactive phase spray formulation coating reacts with dust deposits on the thermal barrier coating.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for training a conversational recommendation system. An embodiment generates a probabilistic pseudo-user neural network model based on at least one interest probability distribution corresponding to a pseudo-user profile. The embodiment trains, using the pseudo-user neural network model, the conversational recommendation system to learn a recommendation policy, where the conversational recommendation system includes an interest-exploration engine and a prompt-decision engine. The training includes performing an iterative learning process that includes selecting an interest-exploration strategy based on one or more of the following: an interest-exploration policy, an earlier pseudo-user response generated by the pseudo-user neural network model, content data, and pseudo-user interaction history.