Towards Personalized Similarity Search for Vector Databases

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00140292" target="_blank" >RIV/00216224:14330/25:00140292 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-75823-2_11" target="_blank" >http://dx.doi.org/10.1007/978-3-031-75823-2_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-75823-2_11" target="_blank" >10.1007/978-3-031-75823-2_11</a>

Result language
angličtina
Original language name
Towards Personalized Similarity Search for Vector Databases
Original language description
The importance of similarity search has become prominent in the fast-evolving vector databases, which apply content embedding techniques on complex data to produce and manage large collections of high-dimensional vectors. Processing of such data is only possible by using a similarity function for storage, structure, and retrieval. However, if multiple users access the collection, their views on similarity can differ as similarity, in general, is subjective and context-dependent. In this article, we elaborate on the problem of a similarity search engine implementation, where users use a common index but search with personalised views of similarity, implemented by a possibly different similarity model. Specifically, we define a foundational theoretical framework and conduct experiments on real-life data to confirm the viability of such an approach. The experiments also indicate future research directions needed to propose and implement an effective and efficient personalised similarity search engine.
Czech name
—
Czech description
—

Project
<a href="/en/project/VK01010147" target="_blank" >VK01010147: Automated digital data forensics lab for complex crime detection</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
17th International Conference on Similarity Search and Applications (SISAP 2024)
ISBN
9783031758225
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
14
Pages from-to
126-139
Publisher name
Springer
Place of publication
Cham
Event location
Providence, RI, USA
Event date
Jan 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001422992900011

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