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Neural methods for IR and RecSys @ IJCNN 2025

Special Session on Neural methods for Information Retrieval and Recommender Systems

More information to come: stay tuned!

Find the important dates

here⁠

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Find the submission instructions

here⁠

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Overview

The landscape of information retrieval (IR) and recommender systems (RecSys) has been fundamentally reshaped by advances in neural methods.

Leveraging deep learning, transformational architectures, and advanced neural embeddings, the IR and RecSys fields are witnessing rapid innovations that enable more personalised, context-aware, and accurate recommendations and retrieval results.

This special session aims to provide a platform for leading researchers to share new insights into the application of neural methods in IR and RecSys, and to foster discussions on recent advances and unexplored areas of research.

Important Dates

Submission Deadline: January 15th, 2025

Acceptance Notification: March 31st, 2025

Camera-ready Submission Deadline: March 31st, 2025

Conference: June 30 - July 5 2025

Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone.

Topics of Interest

Topics of interest include but are not limited to:

Neural architectures tailored for IR and RecSys.

Scalability, interpretability, and efficiency in neural models.

Retrieval-augmented generation and neural recommender systems.

Ethical considerations, fairness, and bias in neural-driven IR and RecSys.

Submission instructions

Format: Each paper is limited to 8 pages, including figures, tables, and references.

Platform:

https://cmt3.research.microsoft.com/IJCNN2025/Submission/Index⁠

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Submission Instructions: Select the Special Session “Neural Methods for IR and RecSys” as the primary subject area when submitting.

Submissions will undergo the same rigorous peer-review process as regular conference papers. Accepted papers will be published in the IJCNN 2025 conference proceedings.

Find the complete submission guidelines here:

https://2025.ijcnn.org/authors/initial-author-instructions⁠

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Organizer

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Federico Siciliano

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