Embedding-Based Item Search via Vector Space Navigation
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Solution Overview
Problem
Conventional search systems for item repositories rely on explicit metadata, which can be unintuitive for users who do not know the correct search terms, and fail to provide an efficient way to navigate through the features of items in a large repository.
Innovation Solution
A search system that navigates an item store based on an embedding space, where items are represented by embeddings that encode their features as numerical vectors, allowing users to explore items by interpolating between selected items in the embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search systems use explicit metadata for searching, then search results can be retrieved based on keyword matching, but users who do not know the correct search terms cannot efficiently find items
Solution Approach 1:
The patent introduces an embedding space as an intermediary representation between items and search queries. Instead of requiring direct keyword matching, the system transforms both items and user intentions into embedding vectors, allowing for intuitive navigation and discovery through the embedding space without requiring users to know specific search terms.
Solution Approach 2:
The patent changes the search parameter from explicit metadata keywords to embedding vectors that capture semantic meaning. By representing items in a continuous embedding space, the system enables search through semantic similarity and proximity rather than exact keyword matching, improving ease of operation for users unfamiliar with specific terminology.
2Productivity
If users browse a large item repository manually, then they can explore items in detail, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical browsing with an automated embedding-based search system. Instead of requiring users to manually navigate through large datasets, the system uses computational embeddings to automatically filter, sort, and present relevant items based on their position and relationships in the embedding space, significantly improving search efficiency and reducing browsing time.
3Adaptability or versatility
If the item repository is made larger to provide more options, then user choice increases, but navigation and exploration become more difficult
Solution Approach 1:
The patent introduces a new dimensional representation space (embedding space) to organize items. By projecting items into this higher-dimensional space where semantic relationships are preserved, the system enables intuitive navigation through visualizations and interactions with embedding data, making exploration of large repositories more manageable and less complex.
Data Source
AI summary
A computer system obtains a first embedding in an embedding space, where the first embedding represents a first item in an item store. The computer system identifies, based on at least a second embedding representing a second item in the item store, an item from the item store, wherein the second embedding is in the embedding space, and the identified item is identified based on a position of the second embedding in the embedding space relative to a position of the first embedding in the embedding space. The computer system outputs an identification of the identified item.