Smart Playlist System for Personalized Content Selection
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Solution Overview
Problem
The explosion of content choices in the media and entertainment industry creates a paradox of choice for viewers, making it difficult for them to select relevant content due to the overwhelming number of options available through various sources, including television, the Internet, and video on demand services.
Innovation Solution
A smart playlist system that collects data from viewers' client devices, determines popular content items, and personalizes a list for each viewer by analyzing their viewing history, social network interactions, and preferences, triggering recording and alerting users to high-relevance live broadcasts, thereby aiding content selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content volume and source diversity are increased to provide more entertainment options, then viewer choice and flexibility are improved, but viewer ability to select relevant content deteriorates due to overwhelming options
Solution Approach 1:
The patent introduces a recommendation engine as an intermediary system that collects data from multiple sources (viewer profiles, viewing history, social networks, content metadata) and processes this information to generate personalized content recommendations. This mediator bridges the gap between abundant content availability and ease of selection by filtering and prioritizing content based on relevance to individual viewers.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting viewer interaction data (viewing habits, ratings, social network interactions) and using this feedback to refine and personalize content recommendations. The recommendation engine adapts to viewer preferences over time, improving content selection ease while maintaining high content availability.
2Measurement precision
If data is collected from multiple sources (viewing history, social networks, content metadata) to improve recommendation accuracy, then personalization quality is improved, but system complexity increases
Solution Approach 1:
The recommendation engine is designed as a universal system that handles multiple data types (viewing history, social network data, content metadata, viewer profiles) through a single integrated platform. This multi-functional approach improves recommendation accuracy by considering diverse factors while managing system complexity through unified data processing architecture.
Solution Approach 2:
The patent segments the data collection and processing system into distinct functional modules: data collection from various sources, data storage in databases, data processing through the recommendation engine, and result delivery to viewers. This segmentation allows each component to be optimized independently, improving overall recommendation accuracy while making the complex system more manageable.
Data Source
AI summary
A system for collecting data from different sources is described. In one example embodiment, the system obtains content-related data from a plurality of source computer systems, automatically identifies, based on the content-related data, content items having respective popularity values greater than a predetermined threshold value as popular content items, and automatically generates a list of popular content items based on the popular content items.