Abstract Knowledge graph embedding involves learning representations of entities—the
vertices of the graph—and relations—the edges of the graph—such that the resulting …

Bundle recommendation aims to generate bundles of associated products that users tend to
consume as a whole under certain circumstances. Modeling the bundle utility for users is a …

The Internet's rapid growth has led to information overload, necessitating recommender
systems for personalized suggestions. While content-based and collaborative filtering have …

Large Language Models (LLMs) are emerging as promising approaches to enhance
session-based recommendation (SBR), where both prompt-based and fine-tuning-based …

Recommendation systems are now being used to address the problem of excess
information in several sectors such as entertainment, social networking, and e-commerce …

Recommender systems play an important role in helping people find information and make
decisions in today's increasingly digitalized societies. However, the wide adoption of such …

Social connections are frequently leveraged as supplementary data to enhance
recommendation systems (RS). In real-world scenarios, the relationships between multiple …

Traditional collaborative filtering (CF)-based recommendation systems are often challenged
by data sparsity. The recent research has recognized the potential of integrating new …

This paper introduces a simple and effective form of data augmentation for recommender
systems. A paraphrase similarity model is applied to widely available textual data, such as …

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF)
techniques used in recommender systems due to its effectiveness and ability to deal with …