Towards Personalized Similarity Search for Vector Databases

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Personalized Similarity Search for Vector Databases

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Towards Personalized Similarity Search for Vector Databases

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/VK01010147" target="_blank" >VK01010147: Automatizovaná forenzní laboratoř digitálních dat pro odhalování komplexní trestné činnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

17th International Conference on Similarity Search and Applications (SISAP 2024)

ISBN

9783031758225

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

126-139

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Providence, RI, USA

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001422992900011