Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Review is an essential piece of information that influences users' decisions, but excessively
long reviews not only degrade the user experience but also affect the accuracy of the …

Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …

Real recommendation systems contain various features, which are often high-dimensional,
sparse, and difficult to learn effectively. In addition to numerical features, user reviews …

Explainable Recommendation has been gaining attention over the last few years in industry
and academia. Explanations provided along with recommendations in a recommender …

Explainable recommender systems can explain their recommendation decisions, enhancing
user trust in the systems. Most explainable recommender systems either rely on human …

Providing explanations within the recommendation system would boost user satisfaction and
foster trust, especially by elaborating on the reasons for selecting recommended items …

Given a recommender system based on reviews, the challenges are how to effectively
represent the review data and how to explain the produced recommendations. We propose …

Explainability is an inherent issue of recommender systems and has received a lot of
attention recently. Generative explainable recommendation, which provides personalized …

We apply causal mediation analysis to explain the decision-making process of neural
models for rumour detection on Twitter. Interventions at the input and network level reveal …