Online Concierge Product Scoring With User-Query Anchor Embeddings
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Solution Overview
Problem
Conventional machine-learning models fail to account for context when selecting content to present to a user, often providing irrelevant content due to a lack of comparison between the target user's context and similar users' interactions.
Innovation Solution
An online concierge system uses a product scoring engine that generates user and query embeddings, compares them to product embeddings, and applies a loss function to prioritize interactions based on a hierarchy, ensuring relevant product presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional machine-learning models select content based on similar users' interactions without context comparison, then content selection simplicity is maintained, but content relevance to target user deteriorates
Solution Approach 1:
The patent introduces a new dimension of context embedding that captures user-specific contextual information (search history, preferences, device type, location) alongside user similarity metrics. This multi-dimensional approach allows the system to maintain simplicity in user similarity computation while adding contextual relevance through separate embedding vectors that are integrated during content scoring.
Solution Approach 2:
The patent introduces context embeddings as an intermediary element between user similarity metrics and content selection. These embeddings act as a mediator that translates raw contextual data (search queries, device information, location) into a format that can be integrated with user similarity scores, enabling relevance improvement without directly complicating the core recommendation engine.
2Reliability
If machine-learning models incorporate context comparison between target user and similar users, then content relevance improves, but model complexity increases
Solution Approach 1:
The patent segments the content selection process into distinct components: user similarity computation, context embedding generation, and integrated scoring. Each component processes specific types of data independently (user behavior data, contextual data), reducing the complexity of any single model while maintaining overall system effectiveness through modular architecture.
Solution Approach 2:
The patent employs universal embedding models that can process multiple types of contextual information (search queries, device characteristics, location data, user preferences) through a unified framework. This multi-functional approach allows a single embedding architecture to handle diverse contextual inputs, reducing the need for separate specialized models for each context type.
3Reliability
If the system processes multiple contextual factors (search history, device type, location, preferences), then content relevance improves, but data processing time increases
Solution Approach 1:
The patent pre-computes and stores context embeddings for users based on their historical data (search history, device type, location, preferences) before actual content selection occurs. This preliminary processing transforms raw contextual data into ready-to-use embedding vectors, significantly reducing the computational burden during real-time content recommendation while maintaining comprehensive context analysis.
Data Source
AI summary
An online concierge system selects content for presentation to a user by using a product scoring engine. The product scoring engine generates a user embedding for user data and a query embedding for query data. The product scoring engine generates an anchor embedding based on the user embedding and the query embedding, where the anchor embedding is an embedding in a product embedding space. The product scoring engine compares the anchor embedding to a set of product embeddings to score a set of products for presentation to a user.