Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Specifically, content item recommendations are provided using a machine learning model. The machine learning model is trained using feature vectors that are correlated to one another. The correlated feature vectors include information indicative of a texture and shape intensity of an image. Using the feature vectors (e.g., image or video signatures), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer).

Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Abstract: Systems and methods for real-time matching of promotional content to content that a user is currently consuming. Content that is currently being consumed is classified into descriptive categories, such as by determining a vector of content features where this vector is in turn used to classify the currently-played content. Promotional content having classifications that match the classifications of the currently-played content is then determined. Matching promotional content may then be played for the user in real time. In this manner, systems and processes of embodiments of the disclosure may identify promotional content matching what the user is currently watching, so as to present users promotional content tailored to subject matter the user is currently interested in.

Abstract: Systems and methods for real-time matching of promotional content to content that a user is currently consuming. Content that is currently being consumed is classified into descriptive categories, such as by determining a vector of content features where this vector is in turn used to classify the currently-played content. Promotional content having classifications that match the classifications of the currently-played content is then determined. Matching promotional content may then be played for the user in real time. In this manner, systems and processes of embodiments of the disclosure may identify promotional content matching what the user is currently watching, so as to present users promotional content tailored to subject matter the user is currently interested in.

Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Specifically, content item recommendations are provided using a machine learning model. The machine learning model is trained using feature vectors that are correlated to one another. The correlated feature vectors include information indicative of a texture and shape intensity of an image. Using the feature vectors (e.g., image or video signatures), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer).

Abstract: Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.