Personalized Content Recommendation System Using Multi-Card Media Guide
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Solution Overview
Problem
With the increasing amount of television programming content available, users are overwhelmed and need help identifying relevant content, as existing systems lack effective methods for personalized content recommendations.
Innovation Solution
A content delivery system that provides personalized content recommendations by displaying a media guide with multiple cards, allowing users to select recommendations based on types, subcategories, and specific program names, and executing actions such as watching or recording programs, utilizing a processing unit and memory storage to analyze user data and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the amount of content available from the content delivery system increases, then the variety and quantity of content options improve, but users become overwhelmed and have difficulty identifying relevant content
Solution Approach 1:
The system collects user feedback through ratings, likes, and viewing behavior data, then uses this feedback to refine and personalize content recommendations. The recommendation engine continuously learns from user interactions to improve the accuracy of content suggestions, making it easier for users to find relevant content as the content library grows.
Solution Approach 2:
The system automatically generates personalized content recommendations without requiring manual user input for each recommendation. The recommendation engine self-adjusts by analyzing user behavior patterns and automatically updating recommendations, reducing the effort users need to expend to discover relevant content.
2Productivity
If the system provides personalized content recommendations by analyzing user data and preferences, then user satisfaction and service efficiency improve, but the complexity of the system increases
Solution Approach 1:
The recommendation system is divided into distinct functional modules: a data collection module that gathers user behavior information, a processing module that analyzes the data, and a recommendation generation module that produces personalized suggestions. This segmentation allows each component to be optimized independently while maintaining overall system efficiency.
Solution Approach 2:
The patent introduces a recommendation engine as an intermediary layer between the content delivery system and the user. This intermediary processes user data and content information, then presents personalized recommendations to users, simplifying the interaction while handling the complexity of data analysis internally.
3Adaptability or versatility
If the media guide displays multiple cards with recommendation types, subcategories, and content program names, then user control over viewing experience improves, but the interface complexity increases
Solution Approach 1:
The media guide interface dynamically adapts its display based on user selections and behavior. The system adjusts which recommendation types and subcategories are shown based on user preferences and interaction patterns, providing personalized control without overwhelming users with all possible options simultaneously.
Solution Approach 2:
The patent organizes content recommendations across multiple hierarchical dimensions: recommendation types (e.g., popular, new), subcategories (e.g., genre, format), and individual content programs. This multi-dimensional organization allows users to navigate and control their viewing experience through different layers of filtering and selection.
Data Source
AI summary
Content recommendations may be provided. First, in response to a received input and concurrent with a shrunken programming content, a media guide may be displayed. The media guide may comprise a first card, a second card, and a third card. The first card may comprise a plurality of recommendation types. The second card may comprise a plurality of subcategories corresponding to a selected one of the plurality of recommendation types. The third card may comprise a plurality of content program names corresponding to a selected one of the plurality of subcategories. The plurality of content program names may be in an order. Next, in response to a selection of a one of the plurality of content program names, an action card maybe displayed. Then a selected one of the plurality of actions may be executed.