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Belhassen Bayar

As seen in: Amazon Science
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Articles

Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning

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By Xiaobo Guo, Belhassen Bayar, Srinivasan H. Sengamedu
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (653) Issue with article?

Multi-task learning for reduced popularity bias in multi-territory video recommendations

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By Phanideep Gampa, Belhassen Bayar, Ainur Yessenalina, Farnoosh Javadi
| Amazon Science
Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users’ viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings.
Open in Who Shared Issue with article?

Design principles of robust multi-armed bandit framework in video recommendations

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By Belhassen Bayar, Phanideep Gampa, Zhen Wen, Ainur Yessenalina
| Amazon Science
Current multi-armed bandit approaches in recommender systems (RS) have focused more on devising effective exploration techniques, while not adequately addressing common exploitation challenges related to distributional changes and item cannibalization. Little work exists to guide the design of robust bandit frameworks that can address these frequent challenges in RS.
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