Systems and methods for generating and presenting content recommendations to new users during or immediately after the onboarding process, before any history of the new user's viewed content is available. A machine learning or other model may be trained to determine clusters of content genre values corresponding to genres of content watched by viewers. Clusters are thus associated with popular groupings of content genres viewed by many users. Clusters representing popular groupings of content genres may be selected for new users, and content corresponding to the selected clusters may be recommended to the new users as part of their onboarding process. A sufficient amount of content may be selected to fully populate any content recommendation portion of a new user onboarding page

The present disclosure provides a computer-implemented method and system for real-time filtering of an inappropriate content from a plurality of video segments. The method includes a first step of receiving multimedia content. In addition, the method includes another step of segmenting the multimedia content in real-time. Further, the method includes yet another step of identifying the inappropriate content in real-time. Furthermore, the method includes yet another step of filtering of the plurality of video segments in real-time. Moreover, the method includes yet another step of displaying an appropriate video content in real-time.

Systems and associated methods are described for providing content recommendations. The system accesses content item consumption data for a plurality of users subscribed to a media service. Then, the system determines that a first subset of the plurality of users has unsubscribed from the media service and that a second subset of the plurality of users has not unsubscribed from the media service. The system identifies a time slot typical for the first subset of users and atypical for the second subset of users based on content item consumption data of the first subset of users and content item consumption data of the second subset of users. In response to determining that a user is consuming a first content item at the identified time slot, the system generates for display a recommendation for a second content item that is scheduled for a different time slot.

Methods and systems for determining and using disliked content are described. The method includes obtaining, by a service provider system, channel viewing data from a user device of a user, pre-processing, by a content analysis unit, the channel viewing data to mitigate inaccuracies and inconsistencies in the channel viewing data, scaling, by the content analysis unit, the pre-processed channel viewing data to highlight temporal dislike aspects of the pre-processed channel viewing data, applying, by the content analysis unit, machine learning algorithms to the scaled channel viewing data to generate a disliked content ratings matrix, and outputting, by the content analysis unit to user interface systems in the service provider system, to provide enhanced user viewing. The user interface systems including a recommender system, an alternate content generation system, or both.

Embodiments of the present disclosure disclose a method and apparatus for recommending a video. A specific implementation of the method includes: finding a recommended video corresponding to a target video from all candidate videos based on similarities of content characteristics of the videos, the target video being a video to be played on a terminal of a user; and sending play information of the recommended video corresponding to the target video to the terminal of the user. The method finds a video similar on video content to the target video that the user desires to view based on the content characteristic of the video, and recommends the video similar on video content to the target video that the user desires to view to the user.

A computer implemented method that includes receiving first user usage data and second user usage data indicative of user usage during consumption of a first content and of a second content, respectively. The method includes determining a first user usage pattern for the first content and a second user usage pattern for the second content based on the first user usage data and on the second user usage data, respectively. The method determines potential content overlap between the first content and the second content based on a comparison of the first usage pattern and the second usage pattern and performs an optimization operation based on the potential content overlap.

Systems and methods are presented herein for recording portions of a media asset relevant to recording criteria. A media application receives input indicating the recording criteria and identifying a first keyword. The media application accesses a data structure to identify a first node associated with the first keyword. The data structure includes the first node and a plurality of nodes connected to the first node via a plurality of paths. The media application receiving audio component data for a portion of the media asset extracts a term from the audio component data, and identifies a second node in the data structure that is associated with the extracted term. The media application calculates a path score for the portion of the media asset based on a path size in the data structure between the first node and the second node. When the score is high enough, the portion of the media asset is recorded.

A method and system for editing a video on the basis of a context obtained using artificial intelligence. According to an embodiment of the present invention, the method for editing a video may comprise the steps of: establishing a plurality of contexts for an input video by analyzing the video through an artificial intelligence model, and taking, from the video, a plurality of clips corresponding to the established plurality of contexts; generating a user interface through which to receive a certain context selected from the plurality of contexts; providing, through the generated user interface, a plurality of clips that has been taken from the video for the context selected from the plurality of contexts through the generated user interface; and generating a highlight image for the selected context for the video by merging clips together that have been selected from the plurality of clips provided through the user interface.

Systems and methods for detecting a conflict between viewing selections of two users before viewing a media asset. In some aspects, the method comprises receiving an audio input through an audio channel, detecting a first utterance from a first user and a second utterance from a second user in the input of the audio channel, parsing the first utterance and the second utterance, analyzing the first utterance and the second utterance to determine context about the first media asset and the second media asset, and presenting a conflict to the users to display on the media asset.

A system for customizing text messages in modifiable videos of a multimedia messaging application (MMA) is provided. In one example embodiment, the system includes a processor and a memory storing processor-executable codes, wherein the processor is configured to analyze recent messages of a user in the MMA to determine a context of the recent messages; determine, based on the context, a customized text message; select, based on the context, a list of relevant modifiable videos from a database configured to store modifiable videos, the modifiable videos being associated with preset text messages; replace the preset text messages in the relevant modifiable videos with the customized message; and render the list of relevant modifiable videos for viewing and selecting by the user, the rendering including displaying the customized text message in the relevant modifiable videos.