Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

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: Systems and methods for intuitive search and recommendation including a content comprehension engine executing on a computer processor and configured to: receive a recommendation request identifying a source content item; generate a first embedding for the source content item in a first embedding space from content metadata and contextual data; apply a trained neural projection model to map the first embedding to a second embedding space, thereby producing a projected embedding; compute, for content item models stored in a repository, a similarity score between the projected embedding and the content item model, each content item model including word-vector collaborative-filtering representations of an available content item; select, based on the similarity scores, a subset of the content item models; and output a result set including the available content items corresponding to the subset and ordered by the similarity scores.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s).

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.

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.

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.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for a content acquisition system to recommend for acquisition a subset of content items selected from a set of content items available for purchase in relation to a content recommendation system currently used in a media environment. The content acquisition system may include a content recommendation system simulator to estimate an impact function value for a potential subset of content items of the set of content items available for purchase based on the currently used content recommendation system. Afterwards, an acquisition recommender can recommend for acquisition a subset of content items based on an optimized objective function value calculated based on an optimization model while meeting one or more budget constraints.

Abstract: System and methods for intuitive search operation results using machine learning including: identifying a first candidate content item matching a content item search request; identifying a first content item model corresponding to the first candidate content item including word vector collaborative filtering representations of the first candidate content item; identifying a set of content item models where each: is associated with at least one corresponding available content item, and includes word vector collaborative filtering representations; applying deep neural learning to compare the first content item model with the set of content item models to generate a subset of the content item models most relevant to the first content item model; generating a result set of available content items corresponding to the subset of the content item models most relevant to the first content item model; and providing the result set of available content items.

Abstract: System and methods for cold-starting content on a platform using machine learning including: identifying content metadata and contextual data both corresponding to a target content item; generating a target content item model by applying deep neural learning that: applies a word vector embedding operation to the content metadata to generate a collaborative filtering representation of the content metadata, applies a word vector embedding operation to the contextual data to generate a collaborative filtering representation of the contextual data, and bridges the collaborative filtering representations of the content metadata and the contextual data to generate the target content item model; applying deep neural learning to compare the target content item model with a set of existing content item models; determining cold-start characteristics of the target content item based on the comparison; and providing the cold-start characteristics for distribution management of the target content item.