Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from
graph-structured data for recommendation. However, most existing GNN-based …

Social media platforms have become vital spaces for public discourse, serving as modern
agor\'as where a wide range of voices influence societal narratives. However, their open …

Graph Neural Networks (GNNs) are powerful machine learning models that excel at
analyzing structured data represented as graphs, demonstrating remarkable performance in …

Path-based explanations provide intrinsic insights into graph-based recommendation
models. However, most previous work has focused on explaining an individual …

In this effort, we propose using the Mamba for handling tabular data in personalized
recommendation systems. We present the\textit {FT-Mamba}(Feature Tokenizer\, $+ …

In the realm of personalization, integrating diverse information sources such as consumption
signals and content-based representations is becoming increasingly critical to build state-of …

Contextual bandits have become an increasingly popular solution for personalized
recommender systems. Despite their growing use, the interpretability of these systems …

In this study, we conducted a comparative evaluation of two popular embedding techniques,
Word2Vec and Node2Vec, to enhance recommendation systems for frequently bought …

Machine Learning (ML) models have achieved significant success in representation
learning across domains like vision, language, graphs, and tabular data. Constructing …