Unified ML Serving Across Tenants for Cold-Start Personalization
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Solution Overview
Problem
Online marketplaces face challenges in providing personalized content to customers across multiple platforms, leading to irrelevant advertisements and decreased user engagement, especially for customers with no previous interaction history.
Innovation Solution
Implementing a unified platform that uses machine learning models to analyze browsing and transaction data across multiple online marketplaces, allowing for standardized data and content recommendations, thereby providing a personalized experience regardless of the customer's previous interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a unified platform with machine learning models is implemented to provide personalized content across multiple tenants, then user experience and advertisement relevance are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple tenant data systems into a unified platform that consolidates browsing and transaction data across all tenants. This unified data structure enables centralized machine learning model training and deployment, allowing personalized content delivery while managing system complexity through integration rather than separate systems for each tenant.
Solution Approach 2:
The machine learning models trained on unified data from multiple tenants serve universal purposes across all tenants and touchpoints. The same model infrastructure handles content personalization for different tenants, devices, and interaction types, making the system multi-functional and reducing overall complexity despite handling diverse data sources.
2Adaptability or versatility
If machine learning models are trained with session data and transaction data across multiple tenants, then personalized content delivery is achieved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models using aggregated session data and transaction data from multiple tenants before actual content delivery is needed. This advance training enables rapid inference and content generation during user interactions, significantly reducing real-time data processing time while maintaining high personalization capability.
3Productivity
If standardized data structures are implemented across multiple tenants, then model reusability and deployment efficiency are improved, but initial system setup and data standardization effort increase
Solution Approach 1:
The patent implements universal data structures and standardized schemas that serve multiple tenants and various machine learning models simultaneously. This standardization enables models to be trained once and deployed across multiple tenants without customization, dramatically improving deployment efficiency. The initial setup effort is amortized over many tenants and model deployments, making the system increasingly efficient as it scales.
Data Source
AI summary
This application relates to apparatus and methods for providing a unified serving platform that allows for the reusability of machine learning models across a plurality of websites to determine personalized content. For example, a computing device trains a machine learning model with session data identifying browsing events and transaction data identifying purchasing events for a plurality of users. The computing device receives and stores session data and transaction data associated with a first website for the customer. The computing device may then receive a request for content to display to the customer on a second website. The computing device generates label data based on the session data and transaction data associated with the first website, and executes the trained machine learning model with the label data. Based on execution of the trained machine learning model, the computing device generates content to display on the second website, and transmits the content.