Accurate model-based Collaborative Filtering (CF) approaches, such as Matrix Factorization
(MF), tend to be black-box machine learning models that lack interpretability and do not …

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …

Explanations have been shown to increase the user's trust in recommendations in addition
to providing other benefits such as scrutability, which is the ability to verify the validity of …

Recent studies show the benefits of reformulating common machine learning models
through the concept of prototypes–representatives of the underlying data, used to calculate …

Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …

Matrix factorization (MF) has been demonstrated to be one of the most competitive
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …

We propose a collaborative filtering (CF) recommendation framework which is based on
viewing user feedback on products as ordinal, rather than the more common numerical view …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …

Classical matrix factorization approaches to collaborative filtering learn a latent vector for
each user and each item, and recommendations are scored via the similarity between two …

Collaborative filtering (CF) has been widely employed within recommender systems to solve
many real-world problems. Learning effective latent factors plays the most important role in …