Metadata-Driven Recommendation Engine for Multi-Tenant Schema Adaptation
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Solution Overview
Problem
Existing recommendation systems are tailored to specific schemas and use cases, requiring significant effort and manpower to develop separate systems for different applications, such as online shopping and streaming content selection, limiting their adaptability and efficiency.
Innovation Solution
A general-purpose engine for generating recommendation models that can apply to any schema or use case by converting raw data into numerical representations using metadata, embedding items and targets into a shared coordinate space, and utilizing scoring algorithms to rank recommendations, allowing for the reuse of models across various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendation systems are tailored to specific schemas and use cases, then the system can provide accurate and relevant recommendations for that particular application, but significant effort and manpower are required to develop separate systems for different applications
Solution Approach 1:
The patent implements a universal recommendation system framework that can handle multiple schemas and use cases through a common architecture. The system uses schema mapping mechanisms that allow different data schemas (e.g., e-commerce, streaming, social networks) to be transformed into a unified recommendation model structure, enabling one system to serve multiple functions across diverse applications without requiring separate custom-built systems for each use case
2Reliability
If separate recommendation systems are developed for each use case, then the system can be optimized for that specific application, but the effort and manpower required increases significantly
Solution Approach 1:
The patent segments the recommendation system into modular components including schema mapping modules, feature extraction modules, ranking modules, and evaluation modules. Each component can be independently configured and optimized for specific use cases while sharing common infrastructure. This segmentation allows the system to maintain application-specific optimization capabilities through configurable modules while improving overall development efficiency through code reuse and standardized interfaces
3Adaptability or versatility
If a general-purpose recommendation system is created to handle multiple use cases, then adaptability improves, but the system may lose specificity and accuracy for particular applications
Solution Approach 1:
The patent implements local quality by allowing different parts of the recommendation system to have different levels of specialization. The schema mapping layer provides general-purpose adaptation to various data formats, while the feature extraction and ranking layers can be customized with application-specific parameters and algorithms. This enables the system to maintain high accuracy for specific applications through localized optimization while preserving overall versatility through the general-purpose mapping framework
Data Source
AI summary
A method for generating a model for recommendations from an item data set for a target data set includes embedding a set of targets from the target data set in a shared coordinate space using a first embedding function, embedding a first set of items from the item data set in the shared coordinate space using a second embedding function, selecting at least one target from the set of targets, and identifying a second set of items from the first set of items that are proximate to the at least one target as candidates from the recommendations.