88 citations · 108 across the 8 of their papers we have counts for
8 papers
Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation
Shereen Elsayed, Ngoc Son Le, Ahmed Rashed +1
Attribute-aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with…
HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation
Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme
In the context of recommendation systems, addressing multi-behavioral user interactions has become vital for understanding the evolving user behavior. Recent models utilize techniq…
Context-Aware Sequential Model for Multi-Behaviour Recommendation
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. However, many focus on a single beh…
Deep Multi-Representation Model for Click-Through Rate Prediction
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. The primary purpose of recent research emp…
End-to-End Image-Based Fashion Recommendation
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…
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention
Ahmed Rashed, Shereen Elsayed, Lars Schmidt-Thieme
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-…
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Shayan Jawed, Mofassir ul Islam Arif, Ahmed Rashed +8
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financia…
Do We Really Need Deep Learning Models for Time Series Forecasting?
Shereen Elsayed, Daniela Thyssens, Ahmed Rashed +2
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…