Synthetic Item Retrieval for Low-Latency Listing Recommendations
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Solution Overview
Problem
Existing item retrieval systems on listing platforms face inefficiencies due to repetitive user queries and excessive computing resource consumption, particularly when user activity information is limited, leading to increased network latency, storage device I/O, and component wear.
Innovation Solution
An item retrieval system utilizing a generative model to generate synthetic items, which are used to identify and rank item listings, reducing the need for repetitive user queries by providing relevant recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search and recommendation systems are used to retrieve items from a large catalog, then users can find relevant items, but the system consumes excessive computing resources and experiences increased network latency
Solution Approach 1:
The system pre-generates synthetic item descriptions using a generative model before users perform searches. These synthetic items are stored in advance and can be quickly retrieved during search operations, eliminating the need to generate descriptions on-demand and reducing both computing resource consumption and network latency during actual search operations.
Solution Approach 2:
The system creates synthetic copies of item descriptions generated by a generative model. These synthetic items are not exact replicas but rather generated alternatives that capture the essential characteristics of original items. During search operations, the system can work with these pre-generated synthetic copies instead of processing original item data, reducing the computational burden and improving retrieval efficiency.
2Ease of operation
If users perform repetitive queries to find relevant items, then they can locate desired products, but this increases network latency and component wear
Solution Approach 1:
The system pre-generates multiple synthetic item descriptions and stores them in advance. When users perform searches, the system can quickly retrieve and present relevant synthetic items without requiring users to perform multiple repetitive queries. This preliminary preparation significantly reduces network latency and eliminates the need for repeated search operations.
Solution Approach 2:
The synthetic items act as intermediaries between the original item catalog and user search queries. Instead of users directly querying the original item database repeatedly, the system uses pre-generated synthetic items as a intermediate layer that can be quickly searched and retrieved, reducing network latency and component wear associated with repetitive direct queries.
3Reliability
If the system processes and stores detailed item information for a vast catalog, then comprehensive search results can be provided, but storage device I/O increases and component wear occurs
Solution Approach 1:
The system creates synthetic copies of item descriptions that capture essential characteristics without requiring storage of all original item details. These synthetic representations maintain sufficient information for accurate search and recommendation while significantly reducing the storage requirements and I/O operations needed to manage the vast item catalog.
Solution Approach 2:
The system extracts only the essential characteristics and features from original item descriptions to create synthetic items. This extraction process removes unnecessary detailed information while retaining the key attributes needed for accurate search and recommendation, thereby reducing storage device I/O and component wear while maintaining search result accuracy.
Data Source
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AI summary
Some aspects relate to technologies for using synthetic items generated by a generative model to perform item retrieval for a listing platform based on seed item listings. In some examples, a textual indication of a seed item listing from a listing platform is received. Based on the seed item listing, a textual indication of one or more synthetic items generated by a generative model are obtained. The textual indication of each synthetic item can be generated by the generative model at runtime or previously generated by the generative model and retrieved at runtime using one or more caching techniques. A search is performed on an item listings data store for the listing platform based on the textual indication of the one or more synthetic items to identify one or more item listings. An indication of the one or more item listings is provided for presentation as item listing recommendations.