1 code implementation • 15 Dec 2023 • Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme
Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions.
1 code implementation • 18 Oct 2022 • Shereen Elsayed, Lars Schmidt-Thieme
Click-Through Rate prediction (CTR) is a crucial task in recommender systems, and it gained considerable attention in the past few years.
1 code implementation • 5 May 2022 • Shereen Elsayed, Lukas Brinkmeyer, Lars Schmidt-Thieme
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, and nearest neighbor models.
1 code implementation • 4 Apr 2022 • Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme
This cross-attention allows CARCA to harness the correlation between old and recent items in the user profile and their influence on deciding which item to recommend next.
Ranked #1 on Sequential Recommendation on Amazon Men (using extra training data)
no code implementations • 9 Feb 2022 • Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed, Kiran Madhusudhanan, Shereen Elsayed, Mohsan Jameel, Alexei Volk, Andre Hintsches, Marlies Kornfeld, Katrin Lange, Lars Schmidt-Thieme
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research.
1 code implementation • 6 Jan 2021 • Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Hadi Samer Jomaa, Lars Schmidt-Thieme
In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model.