Multi-phase ranking for content personalization
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Solution Overview
Problem
Conventional content recommendation systems fail to accurately capture users' long-term interests and often provide fragmented representations of user interests due to reliance on short-term interactions and application-centric approaches, leading to ineffective personalization and limited discovery of new interests.
Innovation Solution
The system employs a multiphase ranking method that utilizes a universal interest space defined by concept archives like Wikipedia to create high-dimensional vectors for users and content, allowing for the estimation of affinity and selection of personalized content based on both short-term and long-term interests, while also incorporating context and performance metrics to dynamically update and expand the content pool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content recommendation systems rely on short-term interactions and application-centric approaches, then implementation simplicity is maintained, but user interest representation becomes fragmented and inaccurate
Solution Approach 1:
The patent segments the user interest representation into multiple dimensions including short-term interests, long-term interests, and contextual interests. Each dimension is captured separately through different interaction signals and then integrated to form a comprehensive user profile, resolving the fragmentation issue while maintaining manageable system complexity through modular processing
Solution Approach 2:
The patent introduces a universal interest space that maps user interests across multiple application domains into a unified dimensional framework. This allows interests from different applications to be represented and compared in a common space, transforming the fragmented application-centric views into a coherent multi-dimensional user profile
2Adaptability or versatility
If systems use isolated application settings for user profiling, then individual application performance is optimized, but broad user interest coverage is lost
Solution Approach 1:
The patent creates a universal interest space that serves multiple applications and domains simultaneously. This universal framework allows the system to maintain coherent user profiles across different applications by mapping diverse interaction types into a unified interest representation, enabling both broad coverage and information integration
3Quantity of substance
If CTR is used as the primary measure for user interest, then implementation simplicity is maintained, but comprehensive user interest capture is insufficient
Solution Approach 1:
The patent merges multiple interaction signals including clicks, dwell time, scrolls, and other engagement metrics into a unified user interest measurement framework. By combining these diverse signals and weighting them appropriately, the system captures comprehensive user interests while managing complexity through integrated processing
Data Source
AI summary
Embodiments of the present teachings disclose method, system, and programs for a multi-phase ranking system for implementation with a personalized content system. The disclosed method, system, and programs utilize a weighted AND system to compute a dot product of the user profile and a content profile in a first phase, a content quality indicator in the second phase and a rules filter in a third phase.