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of 109
pro vyhledávání: '"Elsayed, Shereen"'
In the context of recommendation systems, addressing multi-behavioral user interactions has become vital for understanding the evolving user behavior. Recent models utilize techniques like graph neural networks and attention mechanisms for modeling d
Externí odkaz:
http://arxiv.org/abs/2405.09638
Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions. However, many focus on a single behavior, overlooking valuable implicit interactions like clicks and favo
Externí odkaz:
http://arxiv.org/abs/2312.09684
Autor:
Elsayed, Shereen, Schmidt-Thieme, Lars
Click-Through Rate prediction (CTR) is a crucial task in recommender systems, and it gained considerable attention in the past few years. The primary purpose of recent research emphasizes obtaining meaningful and powerful representations through mini
Externí odkaz:
http://arxiv.org/abs/2210.10664
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, an
Externí odkaz:
http://arxiv.org/abs/2205.02923
Publikováno v:
RecSys (2022) 71-80
In sparse recommender settings, users' context and item attributes play a crucial role in deciding which items to recommend next. Despite that, recent works in sequential and time-aware recommendations usually either ignore both aspects or only consi
Externí odkaz:
http://arxiv.org/abs/2204.06519
Autor:
Jawed, Shayan, Arif, Mofassir ul Islam, Rashed, Ahmed, Madhusudhanan, Kiran, Elsayed, Shereen, Jameel, Mohsan, Volk, Alexei, Hintsches, Andre, Kornfeld, Marlies, Lange, Katrin, Schmidt-Thieme, Lars
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aim
Externí odkaz:
http://arxiv.org/abs/2202.04411
Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and air quality. Traditional forecasting models rely on rolling averages,
Externí odkaz:
http://arxiv.org/abs/2101.02118
Akademický článek
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Autor:
Abdelbasset, Walid Kamal, Alrawaili, Saud M., Elsayed, Shereen H., Diana, Tazeddinova, Ghazali, Sami, Felemban, Bassem F., Zwawi, Mohammed, Algarni, Mohammed, Su, Chia-Hung, Chinh Nguyen, Hoang, Mahmoud, Omar
Publikováno v:
In Arabian Journal of Chemistry July 2022 15(7)
Akademický článek
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