FuseRank (Demo) : Filtered Vector Search in Multimodal Structured Data
Paraschakis, Dimitris LU ; Ros, Rasmus LU ; Borg, Markus LU and Runeson, Per LU
(2024)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14948.
p.404-408
- Abstract
We describe and demonstrate our work on multimodal filtered vector search in tabular data. It offers a practical way for businesses to vectorize their product assortments or any other business-critical data and then simultaneously retrieve and filter this information using state-of-the-art similarity search. Our methodology is based on the extended vector space model, with multiple modalities represented as sub-vectors that get concatenated and compared to the query vector via a dot product operation. It is a flexible framework that allows manipulating the influence of each modality on the overall item ranking via modality weights. We share the source code, the demonstration video, and the screenshot of the application. We also provide... (More)
We describe and demonstrate our work on multimodal filtered vector search in tabular data. It offers a practical way for businesses to vectorize their product assortments or any other business-critical data and then simultaneously retrieve and filter this information using state-of-the-art similarity search. Our methodology is based on the extended vector space model, with multiple modalities represented as sub-vectors that get concatenated and compared to the query vector via a dot product operation. It is a flexible framework that allows manipulating the influence of each modality on the overall item ranking via modality weights. We share the source code, the demonstration video, and the screenshot of the application. We also provide a brief description of its main building blocks, the supported data types, and modality filters. The application is bundled with two public datasets and pre-computed text embeddings so that it can be easily run without prior preparation.
(Less)
- author
- Paraschakis, Dimitris
LU
; Ros, Rasmus
LU
; Borg, Markus
LU
and Runeson, Per
LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Information retrieval, Multimodal filtered vector search
- host publication
- Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Bifet, Albert ; Daniušis, Povilas ; Davis, Jesse ; Krilavičius, Tomas ; Kull, Meelis ; Ntoutsi, Eirini ; Puolamäki, Kai and Žliobaitė, Indrė
- volume
- 14948
- pages
- 5 pages
- publisher
- Springer
- conference name
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024
- conference location
- Vilnius, Lithuania
- conference dates
- 2024-09-09 - 2024-09-13
- external identifiers
-
- scopus:85203877698
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783031703706
- 9783031703713
- DOI
- 10.1007/978-3-031-70371-3_29
- project
- Continuous Optimization for Multimodal Data Fusion in Vector Search
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- id
- 3f87a21e-83ae-400b-956c-211f7a4dc6fa
- date added to LUP
- 2024-09-26 13:56:32
- date last changed
- 2024-09-28 02:19:26
@inproceedings{3f87a21e-83ae-400b-956c-211f7a4dc6fa,
abstract = {{<p>We describe and demonstrate our work on multimodal filtered vector search in tabular data. It offers a practical way for businesses to vectorize their product assortments or any other business-critical data and then simultaneously retrieve and filter this information using state-of-the-art similarity search. Our methodology is based on the extended vector space model, with multiple modalities represented as sub-vectors that get concatenated and compared to the query vector via a dot product operation. It is a flexible framework that allows manipulating the influence of each modality on the overall item ranking via modality weights. We share the source code, the demonstration video, and the screenshot of the application. We also provide a brief description of its main building blocks, the supported data types, and modality filters. The application is bundled with two public datasets and pre-computed text embeddings so that it can be easily run without prior preparation.</p>}},
author = {{Paraschakis, Dimitris and Ros, Rasmus and Borg, Markus and Runeson, Per}},
booktitle = {{Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Proceedings}},
editor = {{Bifet, Albert and Daniušis, Povilas and Davis, Jesse and Krilavičius, Tomas and Kull, Meelis and Ntoutsi, Eirini and Puolamäki, Kai and Žliobaitė, Indrė}},
isbn = {{9783031703706}},
issn = {{1611-3349}},
keywords = {{Information retrieval; Multimodal filtered vector search}},
language = {{eng}},
pages = {{404--408}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{FuseRank (Demo) : Filtered Vector Search in Multimodal Structured Data}},
url = {{http://dx.doi.org/10.1007/978-3-031-70371-3_29}},
doi = {{10.1007/978-3-031-70371-3_29}},
volume = {{14948}},
year = {{2024}},
}