Generative Model Output for Semantic 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 increased 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, and a key-value store to reduce latency and resource consumption by providing relevant item listings without repetitive user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search and recommendation systems are used to retrieve items from a large number of listings, then item retrieval functionality is provided, but computing resource consumption increases and latency is high
Solution Approach 1:
The system pre-generates embeddings for queries and items offline before actual retrieval operations. These pre-computed embeddings are stored in embedding stores, allowing the system to avoid expensive real-time embedding computations during user queries, thus reducing latency and computing resource consumption while maintaining retrieval efficiency
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between queries and items. Instead of directly comparing raw queries with item listings, the system transforms both into embedding vectors that capture semantic meaning. This intermediary representation enables efficient similarity computation and reduces the computational burden of traditional text matching methods
2Adaptability or versatility
If traditional search systems operate on keyword-based queries, then simple search functionality is provided, but query understanding is limited
Solution Approach 1:
The system replaces traditional keyword-based mechanical search with semantic embedding-based retrieval. Instead of matching exact keywords or using simple inverted indexes, the system uses neural network-generated embeddings that capture semantic relationships, enabling the system to understand paraphrases, synonyms, and contextual meanings of queries
3Reliability
If recommendation systems provide diverse recommended items, then item variety is increased, but relevance to user intent decreases
Solution Approach 1:
The system uses user interaction feedback (clicks, purchases, views) to refine and update item embeddings and recommendation strategies. This feedback loop allows the system to learn from user behavior and improve the relevance of recommended items over time while maintaining diversity through exploration-exploitation balances in the recommendation algorithm
Data Source
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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.