Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Recent years have witnessed the great accuracy performance of graph-based Collaborative
Filtering (CF) models for recommender systems. By taking the user-item interaction behavior …

With the advancement in recommendation techniques, focus is diverted from just making
them more accurate to making them fairer and diverse, thus catering to the set of less …

The popularity bias problem is one of the most prominent challenges of recommender
systems, ie, while a few heavily rated items receive much attention in presented …

Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Nowadays, sequential recommendations are becoming more prevalent. A user expects the
system to remember past interactions and not conduct each recommendation round as a …

Recently, group recommendations have gained much attention. Nevertheless, most
approaches consider only one round of recommendations. However, in a real-life scenario …

Digital platforms, such as social media and e-commerce websites, widely make use of
Recommender Systems to provide value to users. However, social consequences of such …

In recent times, deep learning methods have supplanted conventional collaborative filtering
approaches as the backbone of modern recommender systems. However, their gains are …

Popularity bias is defined as the intrinsic tendency of recommendation algorithms to feature
popular items more than unpopular ones in the ranked lists lists they produced. When …