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Researcher in recommender systems
Bucher Sahyouni
Bucher Sahyouni is a doctoral researcher in Artificial Intelligence at the University of Surrey. His work studies architecture and objective design for recommender systems, spanning multimodal and sequential learning, fair representation learning, and robust optimisation under sparse implicit feedback.
Featured work
Research projects
2026 Published
DAP - Differential Adjusted Parity
A differentiable adjusted parity loss for learning fair representations without adversarial training.
2026 Preprint
MuSTRec
A multimodal and sequential transformer-based recommender that models item structure alongside short- and long-term user preferences.
2026 Preprint
MuSICRec
A contrastive graph recommender using sequence-item views and ID-guided multimodal fusion to address sparse interaction histories.
2026 Preprint
DSL - Dual-scale Softmax Loss
A softmax loss that adapts competition within and across training instances to improve Top-K recommendation and robustness.
Writing
Recent articles
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What my thesis taught me about recommender systems
A draft reflection on recommender systems research, evaluation, and the practical lessons of thesis work.
Draft
Why sparse implicit feedback is difficult
A draft explanation of why implicit-feedback recommendation is challenging when most user-item pairs are unobserved.
Draft
Popularity bias in recommender systems: a simple explanation
A draft plain-language explanation of popularity bias and why exposure matters in recommendation.
Reading
Recently read books
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