# Research Publications > Source: https://www.recombee.com/research-publications > 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](https://www.recombee.com/research) / Publications # 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. 2025 [![Cover - SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations](https://www.recombee.com/img/publications/sagea-sparse-autoencoder-based-group-embeddings-aggregation-for-fairness-preserving-group-recommendations.png)](https://dl.acm.org/doi/10.1145/3705328.3759322) ## [SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations](https://dl.acm.org/doi/10.1145/3705328.3759322) Improving Group Recommendations with Sparse Autoencoders by Balancing Accuracy, Fairness, and Efficiency. 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. 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. 2025 [![Cover - Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback](https://www.recombee.com/img/publications/conv4rec-a-1-by-1-convolutional-autoencoder-for-user-profiling-through-joint-analysis-of-implicit-and-explicit-feedback.png)](https://ieeexplore.ieee.org/abstract/document/11159277) ## [Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback](https://ieeexplore.ieee.org/abstract/document/11159277) Jointly Learning Implicit and Explicit feedback. 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. 2025 [![Cover - Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets](https://www.recombee.com/img/publications/probabilistic-modeling-learnability-and-uncertainty-estimation-for-interaction-prediction-in-movie-rating-datasets.png)](https://dl.acm.org/doi/full/10.1145/3705328.3759332) ## [Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets](https://dl.acm.org/doi/full/10.1145/3705328.3759332) Towards Accurate Uncertainty and Test-set Retrieval Performance Estimation. 2025 [![Cover - Mitigating Risks in Online Semantic Search](https://www.recombee.com/img/publications/mitigating-risks-in-online-semantic-search.png)](https://dl.acm.org/doi/10.1145/3699682.3728329) ## [Mitigating Risks in Online Semantic Search](https://dl.acm.org/doi/10.1145/3699682.3728329) Open Dataset for Harmful and Sensitive Query Alignment. 2025 [![Cover - Multitask Learning for Triplet Analysis](https://www.recombee.com/img/publications/multitask-learning-for-triplet-analysis.png)](https://www.sciencedirect.com/science/article/abs/pii/S0957417424030549) ## [Multitask Learning for Triplet Analysis](https://www.sciencedirect.com/science/article/abs/pii/S0957417424030549) Proposition of a multitask learning approach for the triple odd-one-out problem in cognitive sciences. 2025 [![Cover - beeFormer: transformer for recommender systems](https://www.recombee.com/img/publications/beeformer-transformer-for-recommender-systems.png)](https://doi.org/10.1145/3640457.3691707) ## [beeFormer: transformer for recommender systems](https://doi.org/10.1145/3640457.3691707) Improve recommendation of cold start items by training transformers on interactions. 2024 [![Cover - Advanced popularity models for curiosity detection](https://www.recombee.com/img/publications/advanced-popularity-models-for-curiosity-detection.png)](https://doi.org/10.1145/3589334.3645473) ## [Advanced popularity models for curiosity detection](https://doi.org/10.1145/3589334.3645473) Detecting and measuring popularity rates among loyal and curious audiences for online items. 2024 [![Cover - Enhancing local and regional recommendations](https://www.recombee.com/img/publications/enhancing-local-and-regional-recommendations.png)](https://doi.org/10.1145/3656641) ## [Enhancing local and regional recommendations](https://doi.org/10.1145/3656641) Enhance recommendations by aligning them more closely with local preferences and region-specific tastes. 2024 [![Cover - Constrained matrix completion](https://www.recombee.com/img/publications/constrained-matrix-completion.png)](https://proceedings.mlr.press/v235/ledent24a.html) ## [Constrained matrix completion](https://proceedings.mlr.press/v235/ledent24a.html) Enhanced matrix completion methods with new constraints, improving prediction accuracy and efficiency through theoretical analysis and practical experiments. 2024 [![Cover - Context aware recommendation](https://www.recombee.com/img/publications/context-aware-recommendation.png)](https://ieeexplore.ieee.org/document/10496217) ## [Context aware recommendation](https://ieeexplore.ieee.org/document/10496217) Proposing a cognitive modeling approach that predicts selections from item triplets while providing interpretable context and item representations. 2024 [![Cover - LLM alignment with cognitive processes](https://www.recombee.com/img/publications/llm-alignment-with-cognitive-processes.png)](https://doi.org/10.1145/3709148) ## [LLM alignment with cognitive processes](https://doi.org/10.1145/3709148) Proposition and analysis of a methodology for assessing alignment of large language models with cognitive processes. 2024 [![Cover - Minimum item exposure guarantees](https://www.recombee.com/img/publications/minimum-item-exposure-guarantees.png)](https://www.sciencedirect.com/science/article/pii/S0957417423016664) ## [Minimum item exposure guarantees](https://www.sciencedirect.com/science/article/pii/S0957417423016664) Proposing a method to enhance the fairness (by dealing with bias) of item exposure in recommendation lists. 2024 [![Cover - Improved inductive matrix factorization](https://www.recombee.com/img/publications/improved-inductive-matrix-factorization.png)](https://ojs.aaai.org/index.php/AAAI/article/view/26018) ## [Improved inductive matrix factorization](https://ojs.aaai.org/index.php/AAAI/article/view/26018) Proposition of a method for improving inductive matrix factorization, achieving better accuracy, especially in noisy or incomplete data scenarios. 2023 [![Cover - Improving matrix factorization for recommendation](https://www.recombee.com/img/publications/improving-matrix-factorization-for-recommendation.png)](https://ieeexplore.ieee.org/document/10177891) ## [Improving matrix factorization for recommendation](https://ieeexplore.ieee.org/document/10177891) Development of a method for improving matrix-factorization-based recommendations by adjusting uncertainty in the feedback process by using side information. 2023 [![Cover - GNN enhanced matrix factorization](https://www.recombee.com/img/publications/gnn-enhanced-matrix-factorization.png)](https://doi.org/10.1145/3604915.3610654) ## [GNN enhanced matrix factorization](https://doi.org/10.1145/3604915.3610654) Proposition of a technique to enhance recommendations from matrix factorization by incorporating uncertainty adjustments in the feedback mechanism through the use of graph neural networks. 2023 [![Cover - Bridging Offline-Online Evaluation](https://www.recombee.com/img/publications/bridging-offline-online-evaluation.png)](https://www.sciencedirect.com/science/article/pii/S0957417423016664) ## [Bridging Offline-Online Evaluation](https://www.sciencedirect.com/science/article/pii/S0957417423016664) Proposing a method to bridge offline-online evaluation in time-dependent and popularity contexts. 2023