Dynamic Content Graph for Scalable Recommendation Systems
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Solution Overview
Problem
Existing recommendation systems are inefficient for ever-changing content, particularly content available only at specific times, as they rely on static content graphs that require high initial load and do not adapt well to dynamic changes.
Innovation Solution
A method and system that create a base content set with attributes, determine similarity scores using non-sorted, sorted categorical, and numerical similarity functions, and rank recommendations based on weighted sums of these scores, incorporating future availability and user preferences, while utilizing a map-reduce architecture for scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional set-intersection algorithm is used to build a content graph from metadata similarity, then the graph can be constructed with complete content sets, but the initial computational load is high and the system does not adapt well to dynamic content changes
Solution Approach 1:
The patent segments the content graph construction into multiple similarity functions (non-sorted categorical, sorted categorical, and numerical similarity functions) that can be applied independently to different content attributes. This allows parallel processing and reduces the computational burden of building the complete graph, while still achieving comprehensive content similarity assessment.
Solution Approach 2:
The system transitions from a static content graph built on complete content sets to a dynamic graph that can be incrementally updated as content becomes available or expires. The graph structure adapts to changing content availability, allowing the recommendation system to remain responsive without requiring complete recalculation of the entire graph.
2Device complexity
If a static content graph is used for recommendations, then the system is simpler to implement, but it cannot effectively handle ever-changing content available only at specific times
Solution Approach 1:
The patent implements a dynamic content graph that automatically updates as content becomes available or expires. The graph maintains relationships between content items while adapting to changing availability, enabling the system to handle time-limited content without requiring complete system redesign.
Solution Approach 2:
The system pre-computes similarity scores and graph relationships for content items while they are available, storing these computations for later use. This preliminary action allows the system to quickly generate recommendations when content is available without performing heavy computations in real-time, balancing simplicity with adaptability.
3Measurement precision
If multiple similarity functions are applied to determine content similarity, then the recommendation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides the similarity assessment into three distinct similarity functions that operate on different content attributes: non-sorted categorical similarity for unordered attributes, sorted categorical similarity for ordered attributes, and numerical similarity for quantitative attributes. Each function is optimized for its specific attribute type, improving accuracy while keeping individual function complexity manageable.
Solution Approach 2:
The system applies different weighting parameters to each similarity function based on the importance of different content attributes. By adjusting these parameters, the system can emphasize certain similarity aspects over others, tailoring the overall similarity measurement to specific recommendation contexts without requiring complete algorithmic redesign.
Data Source
AI summary
A method includes creating a base content set having attributes indicative of a first program. Selecting a second program to form a first recommendation set, determining a first similarity score between the base content set and the first recommendation set, and providing a recommendation based on the similarity score.