Contextual Bandit Model Selection for Content Delivery
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Solution Overview
Problem
Existing computer user interfaces fail to provide personalized content recommendations that consider user preferences, behavior, and context, leading to inefficient content presentation.
Innovation Solution
A contextual bandit system that employs a machine learning model, referred to as an oracle, and an explore-exploit algorithm to select a suitable machine learning model from a plurality of available models, based on contextual information specific to the user, to determine personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are used to provide personalized content recommendations, then content relevance and user engagement improve, but system complexity increases
Solution Approach 1:
The system segments the content recommendation task by maintaining multiple specialized machine learning models (e.g., collaborative filtering model, content-based model, hybrid model) that each handle different aspects of recommendation. The contextual bandit system then selects the most appropriate model segment for each user context, improving recommendation accuracy while managing complexity through modular organization.
Solution Approach 2:
The contextual bandit system acts as an intermediary layer between the user context and multiple machine learning models. It receives contextual information, selects the most suitable ML model based on the context, and routes the request accordingly. This intermediary structure allows the system to leverage multiple models without requiring complex integration logic throughout the entire system.
2Ease of operation
If curated priority lists are used for content selection, then system simplicity is maintained, but personalization and user preference consideration are lost
Solution Approach 1:
The system transitions from static curated priority lists to dynamic model selection based on contextual bandits. The contextual bandit continuously learns from user interactions and adapts which ML model to deploy for each user and context, enabling personalization while maintaining operational simplicity through automated decision-making rather than manual curation.
Solution Approach 2:
The contextual bandit system incorporates feedback loops where user interactions with recommended content are used to update the bandit policy. This feedback mechanism allows the system to automatically learn which ML models work best for different user segments and contexts, replacing static curated lists with adaptive, data-driven model selection that personalizes recommendations.
Data Source
AI summary
A processor may receive user information for a request payload from an external device and data describing a plurality of user interface (UI) elements configured to be presented in a UI of the external device. The processor may select a machine learning (ML) model from a plurality of ML models using a contextual bandit ML model that is trained based on the user information. The processor determines at least one recommended user interface (UI) element with a selected ML model, based on the user information and the data describing the plurality of UI elements. The at least one recommended UI element may be presented in the UI of the external device. The processor may receive event data indicating a user interaction with the at least one recommended UI element in the UI of the external device. The contextual bandit ML model may be re-trained based on the event data.