Content Viewing Tracking for Program Guide Filtering
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Solution Overview
Problem
Content providers face challenges in quickly and accurately producing content recommendations that fit a user's daily schedule, while maximizing the time available for accessing these recommendations.
Innovation Solution
A method that determines whether a user has viewed content, generates an indication of viewing status displayed with a program guide, and filters content based on user viewing history and interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content providers track and analyze user viewing behavior to produce accurate content recommendations, then recommendation accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by tracking and storing user viewing behavior data in advance (viewing history, preferences, patterns) before recommendation requests occur. This pre-processing enables fast, accurate recommendations without complex real-time analysis, resolving the contradiction between recommendation accuracy and system complexity.
Solution Approach 2:
The system serves itself by automatically analyzing its own collected viewing data to generate recommendations. The tracking system and recommendation engine work autonomously using stored user behavior data, eliminating the need for complex external analysis systems while maintaining high recommendation accuracy.
2Ease of operation
If the system displays viewing status indications with program guide graphics, then user engagement improves, but display complexity and information overload increase
Solution Approach 1:
The system applies local quality by adding viewing status indications selectively at specific locations in the program guide interface where they provide maximum value. Rather than uniformly complicating the entire display, indications are placed locally next to relevant content items, improving user engagement without overwhelming the overall display design.
Solution Approach 2:
The system uses color changes and visual indicators (icons, graphics) to convey viewing status information efficiently. These visual cues provide rich information in minimal space, enhancing user engagement while maintaining display simplicity through intuitive visual language rather than complex text or multiple indicators.
3Measurement precision
If the system filters content based on user viewing history and interests, then content relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering by pre-processing and categorizing content based on stored user viewing history and preferences before actual recommendation requests. This advance preparation enables instant retrieval of relevant content without time-consuming analysis during user interactions, resolving the contradiction between content relevance and processing time.
Solution Approach 2:
The system creates simplified copies or representations of user preferences and content characteristics from detailed viewing history data. These compressed preference profiles enable fast filtering operations while maintaining high content relevance, avoiding the need to process complete viewing histories in real-time.
Data Source
AI summary
This application describes methods, systems, and apparatus for allowing a user to easily determine whether the user and/or other user(s) have viewed content. For example, a program guide may display one or more graphics associated with the content (e.g., a movie poster), and in response to determining whether the user has viewed the content, the program guide may include an indication of whether the user has viewed the content. The program guide may include indications associated with multiple users (e.g., different colors, icons, graphics, or text). The program guide may filter the content based on whether the user has viewed the content (e.g., only display content the user has not viewed). A computing device storing the content may manage recording of the content based on whether the user has viewed the content (e.g., delete the recording if the user has viewed the content). A viewing history of the user may be maintained.