A system for assisting a content consumer to select content services, the system including a processor that is configured to execute a matching algorithm. The matching algorithm comprises interviewing the content consumer for questions in a plurality of categories, determining a point system for answers obtained by the interviewing step, wherein the answers indicate entertainment interests of the content consumer calculating a service score for each content service based on the point system, wherein the service score indicates an agreement between a content service and the entertainment interests of the content consumer and selecting a pre-determined number of content services based on the service score.

Data processing systems and methods are disclosed for combining video content with one or more augmentations to produce augmented video. Objects within video content may have associated bounding boxes that may each be associated with respective RGB values. Upon user selection of a pixel, the RGBA value of the pixel may be used to determine a bounding box associated with the RGBA value. The client may transmit an indicator of the determined bounding box to an augmentation system to request augmentation data for the object associated with the bounding box. The system then uses the indicator to determine the augmentation data and transmits the augmentation data to the client device.

A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

A method and system for digital video content mapping includes receiving and parsing digital video content. Content tokens are defined for tagging content in the digital video content. A cognitive analysis is used to identify content tokens and tag content tokens in the digital video content. A relationship map is created between related tagged content tokens between instances of digital video content using a cognitive analysis. The relationship map indicates the strength of a relationship between the tagged plurality of content tokens and thereby the plurality of instances of digital video content.

Multimedia content to be played on a multimedia player device can be received. Whether the multimedia content contains audience-inappropriate content can be determined. Replacement content corresponding to the audience-inappropriate content can be generated. The generated replacement content can be caused to play on the multimedia player device in lieu of the audience-inappropriate content.

Systems and associated methods are described for providing content recommendations. The system accesses a plurality of recommendation algorithms and assigns a plurality of weight values to each prediction algorithm. Then, the system generates a set of candidate weight combinations, such that each candidate combination includes a weight value assigned to each prediction algorithm. Then requests for content items are received over a predetermined period of time. For each combination, the system generates a set of recommended content items and an evaluation metric that is based on matches with requests. Afterwards, the system replaces a candidate combination that resulted in a generation of a lowest evaluation metric. The aforementioned steps are repeated until the evaluation metrics stop improving. Then display identifiers are displayed for a set of recommended content items generated for a candidate combination with the highest evaluation metric.

An example method performed by a processing system includes receiving a request from a user, wherein the request identifies a plurality of items of source content, and wherein the request indicates that the user would like to generate synthesized content from the plurality of items of source content, retrieving, by the processing system, a plurality of sets of permissions, wherein each set of permissions of the plurality of sets of permissions is associated with one item of source content of the plurality of items of source content, determining, by the processing system, whether the request can be satisfied, based on the plurality of sets of permissions, and automatically generating, by the processing system, the synthesized content using the plurality of items of source content, when the request can be satisfied based on the plurality of sets of permissions.

Disclosed herein are system, method, and computer program product embodiments for automatically and programmatically generating crowdsource trailers. In an embodiment, interactions with streaming content performed by a plurality users who consumed the content are received. A value is assigned to each of the interactions. A plurality of windows of content are identified within the streaming content. The values of the interactions for each of the landing frames within each of the plurality of windows are accumulated. A particular one of the plurality of windows with a highest accumulated value is selected. A trailer for the content is generated based on the selected particular window and provided.

Provided herein are methods and systems for improved accessibility scoring for content items. A predicted accessibility score may be based on a plurality of multimodal features present within a content item. The plurality of multimodal features may include video features (e.g., based on video/image analysis), audio features (e.g., based on audio analysis), text-based features (e.g., based on closed-captioning analysis), features indicated by metadata (e.g., duration, genre, etc.), a combination thereof, and/or the like. A predicted accessibility score for a content item may indicate how accessible the content item may be for persons who are visually impaired, hearing impaired, cognitively impaired, etc., as well as for persons who desire to view content that requires less visual attention and/or audio attention as the case may be.

Systems and associated methods are described for providing content recommendations. The system selects, using a multi-armed bandit solution model, a first plurality of content categories based on a reward score of each content category. The categories are displayed. When a user selects an item from the displayed categories, the system finds all categories that include the selected item, but rewards only the category with the highest score. The system selects, using the multi-armed bandit solution model, the second plurality of content categories based on the updated reward score of each content category. The categories are then displayed. The system may also repeat the steps to refine the multi-armed bandit solution model.