In recent years, the interest in recommendation systems (RSs) has dramatically increased,
as they have become main components of all online stores. The aims of an RS can be …

Transformer-based language models significantly advanced the state-of-the-art in many
linguistic tasks. As this revolution continues, the ability to explain model predictions has …

Collaborative filtering methods for recommender systems tend to represent users as a single
static latent vector. However, user behavior and interests may dynamically change in the …

This paper presents Deep Integrated Explanations (DIX)-a universal method for explaining
vision models. DIX generates explanation maps by integrating information from the …

Two important challenges in recommender systems are modeling users with heterogeneous
taste and providing explainable recommendations. In order to improve our understanding of …

Two main challenges in recommender systems are modeling users with heterogeneous
taste, and providing explainable recommendations. In this paper, we propose the neural …

Recently, there has been growing interest in the ability of Transformer-based models to
produce meaningful embeddings of text with several applications, such as text similarity …

Modern-day recommender systems are often based on learning representations in a latent
vector space that encode user and item preferences. In these models, each user/item is …

In the field of recommender systems, explainability remains a pivotal yet challenging aspect.
To address this, we introduce the Learning to eXplain Recommendations (LXR) framework …

We introduce Stochastic Integrated Explanations (SIX)-a general method for explaining
predictions made by vision models. SIX employs stochastic integration on the internal …