Community Contributions

Advancing AI Through Research & Community

At Recombee, we believe in open innovation and collaborative advancement of AI technologies. Through RecombeeLab, our research division, we actively contribute to the scientific community by publishing state-of-the-art research, supporting PhD students, and openly sharing our methods.

Our partnerships with top universities and our commitment to open-source development reflect our mission to democratize advanced AI technologies.

Research Excellence

Through RecombeeLab, our joint research laboratory with the Faculty of Information Technology at the Czech Technical University in Prague, we're pushing the boundaries of recommendation systems and machine learning. We provide financial support to PhD students and actively collaborate on groundbreaking research projects.

Publications

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Cover - Segment-Aware Analytics for Real-Time Editorial Support in Media Groups: Lessons from The Telegraph

Segment-Aware Analytics for Real-Time Editorial Support in Media Groups: Lessons from The Telegraph

Analytics for Real-Time Editorial Support: Lessons from The Telegraph.
13th International Workshop on News Recommendation and Analytics co-located with the 19th ACM Conference on Recommender Systems
2025
Cover - The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

90% Slimmer Production Embeddings.
Proceedings of the 19th ACM Conference on Recommender Systems
2025
Cover - Recurrent Autoregressive Linear Model for Next-Basket Recommendation

Recurrent Autoregressive Linear Model for Next-Basket Recommendation

Simplicity Wins: Linear Beats Deep in Next-Basket Recommendation.
Proceedings of the 19th ACM Conference on Recommender Systems
2025
Cover - Evaluating Linear Shallow Autoencoders on Large Scale Datasets

Evaluating Linear Shallow Autoencoders on Large Scale Datasets

Scalable Recommendation in Industrial Scale.
ACM Transactions on Recommender Systems
2025

Open Source Projects

ELSA

Scalable linear shallow autoencoder for collaborative filtering.

Github

Repsys

Open-source framework for building and evaluating recommendation systems.

GithubDemo

beeFormer

Advanced transformer architecture optimized for recommendation tasks.

Github

CompresSAE

Sparse compression of embeddings for scalable retrieval.

Github

Community Engagement

We contribute as journal reviewers and serve on the program committees of major conferences.

Conference Support

Proud sponsor and co-organizer of major industry conferences including RecSys, contributing to the global advancement of recommendation systems research.

Non-Profit Collaboration

Supporting organizations like prg.ai and aidetem.cz in their mission to enhance education through AI-assisted personalization.

Making an Impact

Our commitment to open innovation and community support helps advance the field of AI while making cutting-edge technology accessible to researchers and developers worldwide.

Research Support

Funding PhD research and academic collaborations.

Open Source

Sharing advanced AI tools with the community.

Education

Supporting AI-driven educational initiatives.

Collaboration

Would you like to collaborate with us to push the boundaries of recommender systems? Are you interested in doing an industrial master’s or PhD thesis?

Contact us at research@recombee.com or check our Research Opportunities