This workshop aims to unite the research community to address multimodal challenges in search and recommendation. With recent advancements in multimodal LLMs that can democratize multimodal IR, this workshop will serve as a dedicated platform to discuss the latest research and challenges in the field.

Theme : Transforming Search and Recommendations with Multimodal Approaches

Topics

Topics of interest include, but are not limited to:

1. Cross-modal retrieval techniques
   1. Strategies for efficiently indexing and retrieving multimodal data.
   2. Approaches to ensure cross-modal retrieval systems can handle large-scale data.
   3. Development of metrics to measure similarity across different data modalities.
2. Applications of Multimodal Search and Recommendations to Verticals (e.g. E-commerce, real estate)
   1. Implementing and optimizing image-based product searches.
   2. Creating multimodal conversational systems to enhance user experience and make search more accessible.
   3. Utilizing AR to enhance product discovery and user interaction.
   4. Leveraging multimodal search for efficient customer service and support.
3. User-centric design principles for multimodal search interfaces
   1. Best practices for designing user-friendly interfaces that support multimodal search.
   2. Methods for evaluating the usability of multimodal search interfaces.
   3. Personalizing multimodal search interfaces to individual user preferences.
   4. Ensuring multimodal search interfaces are accessible to users with disabilities.
4. Ethical Considerations and Privacy Implications of Multimodal Search and Recommendations
   1. Strategies for ensuring user data privacy in multimodal applications.
   2. Identifying and mitigating biases in multimodal algorithms.
   3. Ensuring transparency in how multimodal results are generated and presented.
   4. Approaches for obtaining and managing user consent for using their data.
5. Modeling for Multimodal Search and Discovery
   1. Multi-modal representation learning
   2. Utilizing GPT-4o, Gemini, and other advanced pre-trained multimodal LLMs
   3. Dimensionality reduction techniques to reduce complexity of multimodal data.
   4. Techniques for fine-tuning pre-trained vision-language models.
   5. 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. The page limits are 8 pages for short papers and 15 pages for long papers.

For instructions and LaTeX/Overleaf/docx templates, refer to CEUR’s submission guidelines, 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 anonymized 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

The deadline for paper submission is August 10, 2024 August 16, 2024 (23:59 P.M. GMT)

Camera Ready Instructions

Please note that at least one author of each accepted paper must register for the workshop and attend in person to present their paper.
If none of the authors can attend, they may nominate an experienced in-person proxy to present their work.

• Authors should prepare a slide deck for a 15-minute presentation.
• After each presentation, 5 minutes will be allocated for follow-up questions.
• Authors can reply to their acceptance emails with the camera-ready version (slide deck).