Networked Recommendation Engines for Cross-Domain Accuracy
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Solution Overview
Problem
Existing machine-learning-based recommendation engines are limited by functional isolation, failing to leverage useful inputs from other Lines of Business (LOBs) and domains, leading to inaccuracies and reduced quality in recommendations.
Innovation Solution
Implementing artificial crowd intelligence by networking recommendation engines across different LOBs and domains, allowing them to share and weight recommendations, and using feedback data to adapt and improve models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation engines operate in isolation within the same LOB or domain, then system complexity is reduced and ease of operation is maintained, but recommendation accuracy and quality deteriorate due to lack of diverse inputs
Solution Approach 1:
The patent combines multiple isolated recommendation engines into a unified networked system. Recommendation engines from different LOBs and domains are merged to share data, models, and insights, enabling cross-domain recommendations while maintaining individual engine autonomy through modular architecture
Solution Approach 2:
The patent creates a universal recommendation framework that serves multiple LOBs and domains simultaneously. A single networked system provides recommendation services across e-commerce, media, finance, and other domains, with each engine adapting to its specific context while benefiting from shared intelligence
2Measurement precision
If recommendation engines network across different LOBs and domains, then recommendation quality improves through diverse inputs, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent introduces intermediary components including centralized data lakes, model registries, and API gateways that mediate between networked recommendation engines. These intermediaries standardize data exchange formats, manage cross-domain communication protocols, and abstract complexity from individual engine operations
Solution Approach 2:
The patent dynamically adjusts operational parameters such as data sampling rates, model training frequencies, and recommendation weights based on domain-specific characteristics and performance metrics. This allows the system to adapt to varying complexities across different LOBs while maintaining ease of operation through automated parameter tuning
3Adaptability or versatility
If recommendation engines network across different LOBs and domains, then diverse inputs improve recommendation quality, but implementation complexity and device complexity increase
Solution Approach 1:
The patent segments the networked recommendation system into independent modular components: data collection modules, processing modules, model training modules, and deployment modules. Each segment handles specific LOBs or domains independently, allowing targeted implementation and reducing overall implementation complexity through divide-and-conquer strategy
Solution Approach 2:
The patent implements dynamic configuration capabilities that allow the recommendation system to adapt its network topology, data flow paths, and model interactions based on real-time performance and domain requirements. This dynamic adaptability reduces implementation complexity by enabling flexible deployment strategies tailored to specific organizational needs
Data Source
AI summary
Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.