Media Recommendation System Using Segmented Search Engines
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Solution Overview
Problem
Users face difficulty in navigating through numerous television and media content options to find programs of interest, often dismissing potentially interesting content due to overwhelming choices and mismatch between actual viewing patterns and self-reported preferences.
Innovation Solution
A system utilizing three independent search engines - Preference Engine, Recommendation Engine, and Curatorial Engine - to dynamically create personalized lists of media content based on current viewing patterns, community preferences, and celebrity/media personality preferences, weighting outputs to provide tailored recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all available media content is presented to users for navigation, then users have access to complete content options, but the number of content options becomes overwhelming and difficult to navigate
Solution Approach 1:
The patent segments the overwhelming list of all available media content into multiple curated lists based on different criteria (user preferences, community recommendations, curatorial selections). Each list contains a manageable subset of content, making navigation easier while collectively covering comprehensive content options across all segments.
Solution Approach 2:
The patent introduces intermediary filtering mechanisms (preference engine, recommendation engine, curatorial engine) that stand between the user and the complete content library. These intermediaries process and filter content based on multiple criteria, presenting users with pre-filtered, manageable lists rather than the raw overwhelming full catalog.
2Adaptability or versatility
If users manually indicate their content interests, then the system can provide personalized recommendations, but the recommendations may not align with actual viewing patterns
Solution Approach 1:
The patent implements feedback loops where the system continuously monitors users' actual viewing behavior and uses this data to refine and update preference profiles. The preference engine learns from observed viewing patterns rather than relying solely on static user inputs, continuously adapting recommendations to match actual preferences with higher precision.
Solution Approach 2:
The system performs self-updating of user preference profiles by automatically analyzing viewing patterns without requiring continuous user input. The preference engine autonomously refines preference data based on observed behavior, eliminating the gap between stated and actual preferences while reducing user burden.
3Measurement precision
If multiple search engines are used to create personalized recommendations, then recommendation quality improves, but system complexity increases
Solution Approach 1:
The patent segments the recommendation system into three specialized search engines (preference engine, recommendation engine, curatorial engine), each handling a specific aspect of recommendation generation. This segmentation allows each engine to focus on its specific function with high precision while the overall system manages complexity through modular architecture.
Solution Approach 2:
The patent merges the outputs of multiple specialized search engines into a unified recommendation system. The preference engine, recommendation engine, and curatorial engine each contribute their specialized capabilities, and their combined outputs are integrated to provide comprehensive, accurate recommendations that leverage the strengths of all three engines.
Data Source
AI summary
A system and method for generating a list of content is disclosed. A processor may store in memory at least one content preference of a user, may store at least one non-user content preference of at least one entity other than the user, may associate the at least one non-user content preference with the user, and may generate the content list based on a combination of the at least one content preference of the user and the at least one non-user content preference for output to the user.