1st Workshop on Multimodal Search and Recommendations (CIKM MMSR ‘24)
Date: October 25, 2024 (Full day workshop)
Venue: ACM CIKM 2024 <https://cikm2024.org/> (Boise, Idaho, United States)
Website: https://cikm-mmsr.github.io/
Organizers: Aditya Chichani, Surya Kallumadi, Tracy Holloway King, Andrei
Lopatenko
Paper submission deadline: August 10, 2024 (23:59 P.M. GMT)
Overview:
The advent of multimodal LLMs like GPT-4o and Gemini has significantly
boosted the potential for multimodal search and recommendations.
Traditional search engines rely mainly on textual queries, supplemented by
session and geographical data. In contrast, multimodal systems create a
shared embedding space for text, images, audio, and more, enabling next-gen
customer experiences. These advancements lead to more accurate and
personalized recommendations, enhancing user satisfaction and engagement.
Topics of interest include, but are not limited to:
Cross-modal retrieval techniques
Strategies for efficiently indexing and retrieving multimodal data.
Approaches to ensure cross-modal retrieval systems can handle
large-scale data.
Development of metrics to measure similarity across different data
modalities.
Applications of Multimodal Search and Recommendations to Verticals (e.g.
E-commerce, real estate)
Implementing and optimizing image-based product searches.
Creating multimodal conversational systems to enhance user experience
and make search more accessible.
Utilizing AR to enhance product discovery and user interaction.
Leveraging multimodal search for efficient customer service and
support.
User-centric design principles for multimodal search interfaces
Best practices for designing user-friendly interfaces that support
multimodal search.
Methods for evaluating the usability of multimodal search interfaces.
Personalizing multimodal search interfaces to individual user
preferences.
Ensuring multimodal search interfaces are accessible to users with
disabilities.
Ethical Considerations and Privacy Implications of Multimodal Search and
Recommendations
Strategies for ensuring user data privacy in multimodal applications.
Identifying and mitigating biases in multimodal algorithms.
Ensuring transparency in how multimodal results are generated and
presented.
Approaches for obtaining and managing user consent for using their
data.
Modeling for Multimodal Search and Discovery
Multi-modal representation learning
Utilizing GPT-4o, Gemini, and other advanced pre-trained multimodal
LLMs
Dimensionality reduction techniques to reduce complexity of
multimodal data.
Techniques for fine-tuning pre-trained vision-language models.
Developing and standardizing metrics to evaluate the performance of
vision-language models in multimodal search.
Submission Instructions:
All papers will be peer-reviewed by the program committee and judged based
on their relevance to the workshop and their potential to generate
discussion. Submissions must be in PDF format, following the latest CEUR
single column format. For instructions and LaTeX/Overleaf/docx templates,
refer to CEUR’s submission guidelines (
https://ceur-ws.org/HOWTOSUBMIT.html#CEURART), reading up to and including
the “License footnote in paper PDFs” section. Use Emphasizing Capitalized
Style for Paper Titles.
Submissions must describe original work not previously published, not
accepted for publication, and not under review elsewhere. All submissions
must be in English. The workshop follows a single-blind review process and
does not accept anonymous submissions. At least one author of each accepted
paper must register for the workshop and present the paper.
Long paper limit: 15 pages.
Short paper limit: 8 pages.
References are not counted in the page limit.
Submit to CIKM MMSR’24:
https://openreview.net/group?id=ACM.org/CIKM/2024/Workshop/MMSR
Contact: Aditya Chichani
E-mail: aditya_chich...@berkeley.edu
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