Title:
Leveraging Low-Dimensional Geometry for Search and Ranking
Leveraging Low-Dimensional Geometry for Search and Ranking
Author(s)
Fenu, Stefano
Advisor(s)
Starner, Thad
Rozell, Christopher J.
Rozell, Christopher J.
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Abstract
There is a substantial body of work on search and ranking in computer science, but less attention has been paid to the question of how to learn geometric data representations that are amenable to search and ranking tasks. Index-based datastructures for search are commonplace, but these discard structural features of the data, often have large memory profiles, and scale poorly with data dimension. Geometric search techniques do exist, but few analogous search datastructures or preprocessing algorithms exist that leverage spatial structure in data to increase search performance. The aim of the research detailed here is to show that leveraging low-dimensional geometry can improve the performance of search and ranking over index-only methods, and that there are dimensionality reduction techniques that can make spatial search algorithms more effective without any additional memory overhead. This work accomplishes these aims by developing methods for: Learning low-dimensional coordinate embeddings explicitly for the purpose of search and ranking; and actively querying and constructing searchable embeddings to minimize user-labeling costs.
This dissertation will further provide scalable versions of these algorithms and demonstrate their effectiveness across a broad range of problem domains including visual, text, and educational data. These performance improvements will allow human-in-the-loop search of larger datasets and enable new applications in preference search and ranking.
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Date Issued
2023-12-06
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Text
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Dissertation