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Filtered multimodal re-ranking for e-commerce

Paraschakis, Dimitris LU ; Ros, Rasmus ; Borg, Markus LU and Runeson, Per LU orcid (2025)
Abstract
This study explores the integration of Large Language Model (LLM) and Information Retrieval (IR) components to enable filtered search over multimodal structured data. We identify core integration challenges and introduce a conceptual framework based on two paradigms: filtered retrieval and filtered re-ranking. With the focus on the latter, we employ RT-3 rank transformation to dynamically adjust the ranking scores according to user-defined filters across multiple attribute modalities numerical , categorical, binary, and spatial. This approach avoids redundant computations, provides fine-grained control over modality pri-oritization, and mitigates over-constrained filtering via soft ranking. We implement our method in a publicly available... (More)
This study explores the integration of Large Language Model (LLM) and Information Retrieval (IR) components to enable filtered search over multimodal structured data. We identify core integration challenges and introduce a conceptual framework based on two paradigms: filtered retrieval and filtered re-ranking. With the focus on the latter, we employ RT-3 rank transformation to dynamically adjust the ranking scores according to user-defined filters across multiple attribute modalities numerical , categorical, binary, and spatial. This approach avoids redundant computations, provides fine-grained control over modality pri-oritization, and mitigates over-constrained filtering via soft ranking. We implement our method in a publicly available web prototype using two real-world datasets, demonstrating its effectiveness in balancing semantic and contextual relevance while empirically validating improved system efficiency in multimodal search scenarios. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Lecture Notes in Business Information Processing (book series)
pages
15 pages
project
WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
language
English
LU publication?
yes
id
617bc232-4835-44d3-9ec3-cd636c632313
date added to LUP
2025-12-09 10:56:35
date last changed
2025-12-12 10:27:32
@inproceedings{617bc232-4835-44d3-9ec3-cd636c632313,
  abstract     = {{This study explores the integration of Large Language Model (LLM) and Information Retrieval (IR) components to enable filtered search over multimodal structured data. We identify core integration challenges and introduce a conceptual framework based on two paradigms: filtered retrieval and filtered re-ranking. With the focus on the latter, we employ RT-3 rank transformation to dynamically adjust the ranking scores according to user-defined filters across multiple attribute modalities numerical , categorical, binary, and spatial. This approach avoids redundant computations, provides fine-grained control over modality pri-oritization, and mitigates over-constrained filtering via soft ranking. We implement our method in a publicly available web prototype using two real-world datasets, demonstrating its effectiveness in balancing semantic and contextual relevance while empirically validating improved system efficiency in multimodal search scenarios.}},
  author       = {{Paraschakis, Dimitris and Ros, Rasmus and Borg, Markus and Runeson, Per}},
  booktitle    = {{Lecture Notes in Business Information Processing (book series)}},
  language     = {{eng}},
  month        = {{09}},
  title        = {{Filtered multimodal re-ranking for e-commerce}},
  year         = {{2025}},
}