The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

Machine learning models have had discernible achievements in a myriad of applications.
However, most of these models are black-boxes, and it is obscure how the decisions are …

While neural recommenders have become the state-of-the-art in recent years, the complexity
of deep models still makes the generation of tangible explanations for end users a …

Recommendation unlearning is an emerging task to serve users for erasing unusable data
(eg, some historical behaviors) from a well-trained recommender model. Existing methods …

The need of increasing trustworthiness and transparency in artificial intelligence (AI)-based
systems that adhere ethical principles of respect for human autonomy, prevention of harm …

The explainability of recommendation systems is crucial for enhancing user trust and
satisfaction. Leveraging large language models (LLMs) offers new opportunities for …

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

Expert recommendation is a central task in question-answering communities that involves
facing several issues, such as quantifying users' expertise and replying propensity …

Counterfactual explanations interpret the recommendation mechanism by exploring how
minimal alterations on items or users affect recommendation decisions. Existing …