Recent advancements in deep learning architecture have increased its utility in real-life
applications. Deep learning models require a large amount of data to train the model. In …

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …

This paper explores the biases inherent in ChatGPT-based recommender systems, focusing
on provider fairness (item-side fairness). Through extensive experiments and over a …

Collaborative filtering is one of the most fundamental topics for recommender systems.
Various methods have been proposed for collaborative filtering, ranging from matrix …

Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF)
used in recommender systems. When few ratings are available, CF models typically fail to …

We address the multimedia recommendation problem, which utilizes items' multimodal
features, such as visual and textual modalities, in addition to interaction information. While a …

Recommender systems (RS) now play a very important role in the online lives of people as
they serve as personalized filters for users to find relevant items from a sea of options. Owing …

Recently, neighborhood-based collaborative filtering has been increasingly used in
personalized recommender systems. However, inevitably, the neighborhood selection is …