Graph-Convolution Query Augmentation for Short-Query Retrieval
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Solution Overview
Problem
Dense retrieval techniques produce poor-quality search results when short input queries are submitted due to insufficient revelation of the underlying query intent.
Innovation Solution
A technique that combines a query embedding with metadata embeddings using a graph convolution operation, ensuring the augmented embedding lies in the same vector space as target-item embeddings, and integrates context information into the query to enhance intent matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dense retrieval is used with short queries, then the retrieval process is simple and fast, but the search result quality is poor due to insufficient query intent revelation
Solution Approach 1:
The system performs preliminary actions by generating metadata embeddings and augmenting the query embedding before the actual retrieval process. This preprocessing step enriches the short query with additional context information from multiple metadata models, enabling better intent revelation without compromising retrieval speed during execution
Solution Approach 2:
The augmented embedding serves as an intermediary between the short query and the target items. By combining the query embedding with metadata embeddings from multiple independent models through a graph convolution operation, the system creates an enhanced representation that bridges the gap between limited query information and comprehensive retrieval requirements
2Measurement precision
If multiple metadata models are integrated to enhance query understanding, then the search result quality improves, but the system complexity increases
Solution Approach 1:
The system segments the complex task of query understanding by employing multiple independent metadata models, each specializing in different aspects of metadata generation. This segmentation allows each model to focus on specific metadata types while the graph convolution operation integrates their outputs, managing overall system complexity through modular organization
Solution Approach 2:
The graph convolution operation serves as a universal integration mechanism that can combine embeddings from any number of independent metadata models. This multi-functional approach allows the system to incorporate diverse metadata sources without requiring separate integration logic for each model, thereby managing complexity through a unified processing framework
3Measurement precision
If context information is integrated into short queries to reveal underlying intent, then the search result quality improves, but the processing time increases
Solution Approach 1:
Metadata embeddings are generated in advance through offline training and indexing processes. During query processing, the system performs efficient lookups and combination operations rather than generating metadata from scratch, thereby reducing real-time processing time while still providing enriched context information for accurate intent matching
4Adaptability or versatility
If independently developed metadata models are integrated, then the system adaptability and scalability improve, but the integration complexity increases
Solution Approach 1:
The graph convolution operation acts as an intermediary layer that standardizes the integration of embeddings from independently developed metadata models. Each model can operate independently with its own training and optimization processes, while the graph convolution provides a unified interface for combining their outputs, thereby managing integration complexity through standardization
Data Source
AI summary
An information retrieval technique uses one or more machine-trained models to generate one or more metadata embeddings. The technique then combines a query embedding with the metadata embedding(s). In some cases, the technique performs this operation using a graph convolution operation. This yields an augmented embedding. The technique then uses the augmented embedding to retrieve at least one item. The augmented embedding lies in the same vector space as target-item embeddings associated with candidate target items. Otherwise, the vector spaces associated with the query embedding and metadata embedding(s) can be different. In some implementations, the technique use dense retrieval, which enables the technique to deliver output results in real time.