Generative Query Embeddings for Low-Latency Item Retrieval
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Solution Overview
Problem
Existing item retrieval systems on listing platforms face inefficiencies in search and recommendation technologies, leading to excessive computing resource consumption, repetitive user queries, and irrelevant item listings, due to limited query understanding, insufficient filtering, and unstructured data handling.
Innovation Solution
An item retrieval system leveraging generative model output to generate text for item listings, using query and item embeddings to identify relevant listings, reducing repetitive queries and improving relevance through offline embedding generation and vector similarity techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search and recommendation systems are used to handle user queries on listing platforms, then item retrieval functionality is provided, but computing resource consumption increases and query understanding is limited
Solution Approach 1:
The system pre-generates embeddings for queries and items offline before actual user interactions. These embeddings are stored in embedding stores for rapid retrieval during runtime. This preliminary action eliminates the need for computationally intensive embedding generation during live query processing, thereby reducing computing resource consumption while maintaining reliable query understanding.
Solution Approach 2:
The patent introduces embeddings as intermediary representations between raw queries/items and the retrieval system. Instead of directly processing raw queries with complex algorithms, the system uses pre-computed embeddings as mediators that capture semantic meaning efficiently. This intermediary layer enables reliable query understanding with minimal runtime computing resources.
2Loss of time
If traditional search systems process user queries in real-time without pre-processing, then responsiveness is maintained, but latency increases due to repetitive query processing
Solution Approach 1:
Embeddings for queries and items are generated and stored in advance during offline processing. When users submit queries, the system performs rapid similarity searches using pre-computed embeddings instead of generating embeddings in real-time. This preliminary action dramatically reduces query processing latency while improving search efficiency through vector similarity comparisons.
Solution Approach 2:
The system creates copies of query and item data in the form of embeddings and stores them in embedding stores. These embedding copies enable fast similarity searches without repeatedly processing the original raw data. The copying approach allows the system to maintain low latency while significantly improving overall search productivity.
3Reliability
If comprehensive item listings are retrieved to ensure relevance, then coverage is improved, but computing resources are excessively consumed
Solution Approach 1:
The patent uses embeddings as intermediary representations that efficiently capture the semantic meaning of queries and items. By comparing embeddings instead of processing full item listings, the system identifies relevant items with minimal computing resources. This intermediary approach maintains high relevance coverage while dramatically reducing computational overhead.
Solution Approach 2:
The system replaces traditional mechanical search processing with vector similarity computations based on embeddings. Instead of using resource-intensive full-text search or complex filtering mechanisms, the system substitutes these with efficient dot-product or cosine similarity calculations on pre-computed embedding vectors, achieving relevant item retrieval with far lower computing resource consumption.
4Measurement precision
If real-time embedding generation is performed for each query, then query specificity is maintained, but processing time increases
Solution Approach 1:
The system pre-generates embeddings for queries and items during offline processing and stores them in embedding stores. When users submit queries, the system retrieves pre-computed embeddings instead of generating them in real-time. This preliminary action maintains query embedding accuracy while eliminating the time-consuming real-time embedding generation process.
Data Source
AI summary
Some aspects relate to technologies for leveraging model output from a generative model to perform item retrieval on a listing platform. In some examples, input for item retrieval is provided to a generative model to produce a model output. A lookup is performed on a key-value store using the model output. If a matching query is found in the key-value store, a query embedding corresponding to the matching query is returned. If a matching query is not found, a query embedding is obtained by generating the query embedding from the model output or using the model output to query a known query index for a known query, which is used to lookup a query embedding in the key-value store. One or more item embeddings are identified based on the query embedding. An output is provided identifying one or more item listings corresponding to the one or more item embeddings.