Unified Recommendation Model With User-Context Embedding Merging

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Solution Overview

Problem

Conventional recommendation systems require separate models for different tasks, leading to increased complexity, resource intensity, and potential conflicts between models, resulting in inconsistent user experiences and delays.

Innovation Solution

A unified recommendation model processes both user features and context features, integrating user embeddings and context embeddings within a single model, with caching mechanisms to reduce latency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If separate models are deployed for different tasks (search, recommendation, contextual recommendations), then each model can be optimized for its specific task, but the system complexity increases, computational resources increase, and model conflicts may occur

Engineering Contradiction:

Improvetask-specific model performanceVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent combines multiple separate recommendation models (search model, personalized recommendation model, contextual recommendation model) into a single unified recommendation model. This unified model processes both user inputs and context inputs through shared embedding layers and merging mechanisms, reducing system complexity while maintaining task-specific capabilities through specialized processing paths within the unified architecture.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The unified recommendation model is designed to perform multiple functions simultaneously - it can handle search queries, generate personalized recommendations, and provide contextual recommendations all through a single model architecture. The model includes specialized processing paths for different task types while sharing common embedding and processing layers, enabling one model to replace multiple specialized models.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If separate models are deployed for different tasks, then each model can be independently trained, but computational resources and maintenance overhead increase significantly

Engineering Contradiction:

Improveindependent model trainingVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent merges multiple model training processes into a single unified training framework. The unified recommendation model is trained on combined datasets that include search queries, user interactions, and contextual information, allowing simultaneous optimization for multiple tasks. This reduces computational resources by eliminating redundant training of separate models while maintaining the ability to independently adjust parameters for different task types through specialized loss functions and training data weighting.

Inventive Principle:

Principle #5Merging (Combining)

3Manufacturing precision

If multiple separate models are used, then task-specific optimization is possible, but synchronization delays occur and user experience consistency deteriorates

Engineering Contradiction:

Improvetask optimization qualityVSAvoidrecommendation delivery delay

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent combines multiple model inference processes into a single unified inference pipeline. When a user input is received, the unified recommendation model processes it through shared embedding layers and generates recommendations in a single pass, eliminating the need to synchronize outputs from multiple separate models. This reduces latency while maintaining task-specific optimization through specialized processing paths within the unified architecture.

Inventive Principle:

Principle #5Merging (Combining)

4Ease of manufacture

If separate models are deployed, then specialized processing for each task is achievable, but resource intensity and maintenance costs increase

Engineering Contradiction:

Improvespecialized processing capabilityVSAvoidresource efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The unified recommendation model is designed as a multi-functional system that can handle different task types (search, personalized recommendation, contextual recommendation) through a single architecture. It includes specialized processing paths and embedding layers for different task types while sharing common computational resources, thereby maintaining specialized processing capabilities while improving resource efficiency by eliminating redundant model instances.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250371601A1Techniques for personalized recommendation using user foundation models

Publication Date: 2025.12.04 NETFLIX INC

AI summary

Techniques for generating recommendations using a recommendation model include receiving one or more user inputs, determining one or more context features based on one or more context inputs, determining one or more user features based on the one or more user inputs, determining one or more user embeddings based on the one or more user features, determining one or more context embeddings based on the one or more context features, merging the one or more user embeddings and the one or more context embeddings to generate one or more merged embeddings; and generating recommendations based on the one or more merged embeddings.