# Research > Source: https://www.recombee.com/research > For the complete site index, see [llms.txt](/llms.txt). To integrate Recombee, see the developer documentation at https://docs.recombee.com/llms.txt. Community Contributions # Advancing AI Through Research & Community ![](https://www.recombee.com/img/research/main.png) 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 [See all ->](https://www.recombee.com/research-publications) [![Cover - Segment-Aware Analytics for Real-Time Editorial Support in Media Groups: Lessons from The Telegraph](https://www.recombee.com/img/publications/segment-aware-analytics-for-real-time-editorial-support-in-media-groups-lessons-from-the-telegraph.png)](https://ceur-ws.org/Vol-4056/) ## [Segment-Aware Analytics for Real-Time Editorial Support in Media Groups: Lessons from The Telegraph](https://ceur-ws.org/Vol-4056/) 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](https://www.recombee.com/img/publications/the-future-is-sparse-embedding-compression-for-scalable-retrieval-in-recommender-systems.png)](https://dl.acm.org/doi/full/10.1145/3705328.3748147) ## [The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems](https://dl.acm.org/doi/full/10.1145/3705328.3748147) 90% Slimmer Production Embeddings. Proceedings of the 19th ACM Conference on Recommender Systems 2025 [![Cover - Recurrent Autoregressive Linear Model for Next-Basket Recommendation](https://www.recombee.com/img/publications/recurrent-autoregressive-linear-model-for-next-basket-recommendation.png)](https://dl.acm.org/doi/full/10.1145/3705328.3759313) ## [Recurrent Autoregressive Linear Model for Next-Basket Recommendation](https://dl.acm.org/doi/full/10.1145/3705328.3759313) 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](https://www.recombee.com/img/publications/evaluating-linear-shallow-autoencoders-on-large-scale-datasets.png)](https://dl.acm.org/doi/10.1145/3748335) ## [Evaluating Linear Shallow Autoencoders on Large Scale Datasets](https://dl.acm.org/doi/10.1145/3748335) Scalable Recommendation in Industrial Scale. ACM Transactions on Recommender Systems 2025 ### Research Posters [See all ->](https://www.recombee.com/research-posters) [![Poster - Active Recommendation for Email Outreach Dynamics](https://www.recombee.com/img/research-posters/active-recommendation-for-email-outreach-dynamics.png)](https://www.recombee.com/img/research-posters/active-recommendation-for-email-outreach-dynamics.png) [![Poster - SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations](https://www.recombee.com/img/research-posters/sagea-sparse-autoencoder-based-group-embeddings-aggregation-for-fairness-preserving-group-recommendations.png)](https://www.recombee.com/img/research-posters/sagea-sparse-autoencoder-based-group-embeddings-aggregation-for-fairness-preserving-group-recommendations.png) [![Poster - Recurrent Autoregressive Linear Model for Next-Basket Recommendation](https://www.recombee.com/img/research-posters/recurrent-autoregressive-linear-model-for-next-basket-recommendation.png)](https://www.recombee.com/img/research-posters/recurrent-autoregressive-linear-model-for-next-basket-recommendation.png) ## Open Source Projects ![](https://www.recombee.com/img/research/elsa.png) ### ELSA Scalable linear shallow autoencoder for collaborative filtering. [Github](https://github.com/recombee/ELSA) ![](https://www.recombee.com/img/research/repsys.png) ### Repsys Open-source framework for building and evaluating recommendation systems. [Github](https://github.com/cowjen01/repsys) [Demo](https://repsys.recombee.net/) ![](https://www.recombee.com/img/research/beeformer.png) ### beeFormer Advanced transformer architecture optimized for recommendation tasks. [Github](https://github.com/recombee/beeformer) ![](https://www.recombee.com/img/research/compressae.png) ### CompresSAE Sparse compression of embeddings for scalable retrieval. [Github](https://github.com/recombee/CompresSAE) ## 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](mailto:research@recombee.com) or check our [Research Opportunities](https://experts.ai/widgets/organizations/193179?opportunity=true)