Dynamic Recommendation System for Content-Type-Specific Personalization
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Solution Overview
Problem
Existing recommendation systems uniformly apply user preferences across all content types, leading to irrelevant or biased recommendations for different content types.
Innovation Solution
A dynamic recommendation system that selects and generates different recommendation configurations based on specific content types, using customized models and user preferences to provide personalized content selections, accounting for time, genre, and user-specific preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform user preferences are applied across all content types, then the recommendation system is simple to implement, but the recommendation relevance deteriorates
Solution Approach 1:
The patent segments user preferences into content-type-specific preferences, creating separate preference models for different content categories (e.g., news, sports, entertainment). This allows the system to tailor recommendations to each content type while maintaining overall system structure, resolving the contradiction between simplicity and relevance.
Solution Approach 2:
The system dynamically selects and applies different user preference models based on the requested content type. Rather than using a static uniform approach, the system adapts its recommendation strategy in real-time based on content category, improving relevance without requiring complete system redesign.
2Measurement precision
If content-type-specific recommendation configurations are used, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal recommendation engine that can handle multiple content types through a single system architecture. The system uses a content-type-specific preference model selector that routes different content types to appropriate preference models, achieving multi-functionality without requiring separate recommendation systems for each content category.
3Ease of operation
If uniform preference application is used, then ease of operation is maintained, but user satisfaction deteriorates
Solution Approach 1:
The system automatically detects the content type and selects the appropriate user preference model without requiring user intervention. The content-type-specific preference selection happens transparently in the background, maintaining ease of operation while improving user satisfaction through more accurate recommendations.
Data Source
AI summary
Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.