Automated ML Recommendations for Siloed User and Content Data
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Solution Overview
Problem
Existing websites and applications face challenges in providing personalized experiences due to distributed and siloed user data, requiring manual effort and specialized expertise for rule-based or machine learning-based recommendations, leading to slow deployment and incorrect recommendations.
Innovation Solution
A computer-implemented method using automated machine learning to generate recommendations by transforming historical user and content data into suitable formats, training a machine learning model with supervised learning, and generating a ranked list of content items based on user interactions and outcomes, with optional clustering for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based or machine learning based recommendation algorithms are implemented, then personalized experiences can be provided to users, but manual effort and specialized expertise are required leading to slow deployment
Solution Approach 1:
The system performs automated machine learning where the recommendation model generates and optimizes its own configuration parameters without human intervention. The model automatically selects features, determines hyperparameters, and iterates through multiple configurations to find optimal settings, enabling self-service deployment of personalized recommendation algorithms.
Solution Approach 2:
The system automatically varies and optimizes multiple parameters including feature selection, model hyperparameters, and algorithm configurations. By systematically changing and evaluating different parameter combinations through automated ML, the system finds optimal settings for personalized recommendations without requiring manual parameter tuning by specialists.
2Adaptability or versatility
If rule-based or machine learning based recommendation algorithms are implemented, then personalized experiences can be provided to users, but specialized expertise is required leading to incorrect recommendations
Solution Approach 1:
The system incorporates feedback loops where the recommendation model's performance is continuously evaluated against actual user interactions and outcomes. The automated ML process uses this feedback to iteratively refine feature selections, adjust hyperparameters, and improve recommendation accuracy, ensuring reliable personalized experiences without requiring expert oversight.
3Adaptability or versatility
If user data is distributed across multiple systems and siloed, then data integration is required before generating personalized experiences, but this increases system complexity
Solution Approach 1:
The automated machine learning system serves as a universal platform that can ingest and process user data from multiple distributed systems and siloed sources. The system automatically handles data integration, feature engineering, and model training across diverse data formats and sources, providing a multi-functional solution that eliminates the need for separate integration processes for each data source.
Data Source
AI summary
Implementations described herein relate to methods, systems, and computer-readable media for automated generation and use of a machine learning (ML) model to provide recommendations. In some implementations, a method includes receiving a recommendation specification that includes a content type and an outcome identifier, and determining model parameters for a ML model based on the recommendation specification. The method further includes generating a historical user feature matrix (FM), generating a historical content feature matrix (FM), and transforming the historical user FM and the historical content FM into a suitable format for the ML model. The method further includes obtaining a target dataset that includes historical results for the outcome identifier for a plurality of pairs of user identifiers and content items of the content type. The method further includes training the ML model using supervised learning to generate a ranked list of content items for each user identifier.