Customized Video Preview Generation for Streaming Content Discovery
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Solution Overview
Problem
The conventional content discovery process for streaming media is inefficient, requiring users to scroll through numerous video program titles, leading to frustration and excessive use of backend server resources and network bandwidth.
Innovation Solution
An efficient content navigation interface that utilizes heuristics and learning engines to generate customized previews for users, reducing the need for extensive screen navigation and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually browse through video program listings to discover content, then users can find content of interest, but the process consumes excessive time and backend server resources
Solution Approach 1:
The system automatically generates and serves customized video previews to users based on their preferences and viewing history, eliminating the need for manual browsing. The preview generation service autonomously selects and assembles relevant video segments, allowing users to quickly assess content relevance without time-consuming navigation through listings.
Solution Approach 2:
The system pre-generates customized previews for multiple video programs in advance, so that when users access the platform, relevant content is already prepared and ready for immediate viewing. This preliminary preparation significantly reduces both user discovery time and real-time server processing loads.
2Adaptability or versatility
If conventional previews are provided to all users, then content information is made available, but the previews do not reflect individual user interests and consume unnecessary resources
Solution Approach 1:
The system tailors preview content to each user's specific preferences, viewing history, and profile characteristics. Different users receive different preview selections from the same video program, with each preview customized to match that user's interests. This localized personalization ensures that each user sees only the most relevant content highlights.
Solution Approach 2:
The preview generation service dynamically adjusts preview parameters such as selected video segments, duration, and content type based on user-specific attributes. By changing these parameters according to user profiles, the system delivers highly relevant previews without requiring users to manually filter through irrelevant content.
3Ease of operation
If users scroll through multiple screens of video content listings, then users can potentially find interesting content, but the process requires excessive user effort and navigation
Solution Approach 1:
The system extracts and presents only the most relevant video content segments in customized previews, removing the need for users to navigate through complete video listings. By taking out only the essential, interest-matching portions of video content and presenting them in condensed preview format, the system dramatically simplifies the user interaction required to discover relevant content.
4Area of stationary object
If standard video previews are rendered in small thumbnails, then content listings can display more items, but the preview quality and user experience are insufficient
Solution Approach 1:
The system transitions from two-dimensional static thumbnails to full-screen or large-format video playback for previews. This dimensional change allows users to view previews in much higher quality and detail without compromising the ability to display multiple content items, as the customized preview selection compensates for the reduced number of simultaneously visible previews.
Data Source
AI summary
An aspect of the disclosure related to methods and systems configured to distribute interactive media, such as videos, streamed or downloaded over a network and to enable efficient content discovery. An aspect relates to enabling a user to interactively navigate through representations of video content by drilling up to broader genre categories or down to narrower genre categories. An aspect relates to the generation of customized content from existing content using a learning engine and user characteristics. The learning engine may comprise a neural network.