Community Contributions / Publications
Publications
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.
2025
SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations
Improving Group Recommendations with Sparse Autoencoders by Balancing Accuracy, Fairness, and Efficiency.
2025
Recurrent Autoregressive Linear Model for Next-Basket Recommendation
Simplicity Wins: Linear Beats Deep in Next-Basket Recommendation.
2025
The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems
90% Slimmer Production Embeddings.
2025
Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback
Jointly Learning Implicit and Explicit feedback.
2025
Evaluating Linear Shallow Autoencoders on Large Scale Datasets
Scalable Recommendation in Industrial Scale.
2025
Probabilistic Modeling, Learnability and Uncertainty Estimation for Interaction Prediction in Movie Rating Datasets
Towards Accurate Uncertainty and Test-set Retrieval Performance Estimation.
2025
Mitigating Risks in Online Semantic Search
Open Dataset for Harmful and Sensitive Query Alignment.
2025
Multitask Learning for Triplet Analysis
Proposition of a multitask learning approach for the triple odd-one-out problem in cognitive sciences.
2025
beeFormer: transformer for recommender systems
Improve recommendation of cold start items by training transformers on interactions.
2024
Advanced popularity models for curiosity detection
Detecting and measuring popularity rates among loyal and curious audiences for online items.
2024
Enhancing local and regional recommendations
Enhance recommendations by aligning them more closely with local preferences and region-specific tastes.
2024
Constrained matrix completion
Enhanced matrix completion methods with new constraints, improving prediction accuracy and efficiency through theoretical analysis and practical experiments.
2024
Context aware recommendation
Proposing a cognitive modeling approach that predicts selections from item triplets while providing interpretable context and item representations.
2024
LLM alignment with cognitive processes
Proposition and analysis of a methodology for assessing alignment of large language models with cognitive processes.
2024
Minimum item exposure guarantees
Proposing a method to enhance the fairness (by dealing with bias) of item exposure in recommendation lists.
2024
Improved inductive matrix factorization
Proposition of a method for improving inductive matrix factorization, achieving better accuracy, especially in noisy or incomplete data scenarios.
2023
Improving matrix factorization for recommendation
Development of a method for improving matrix-factorization-based recommendations by adjusting uncertainty in the feedback process by using side information.
2023
GNN enhanced matrix factorization
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
Bridging Offline-Online Evaluation
Proposing a method to bridge offline-online evaluation in time-dependent and popularity contexts.
2023
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