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

The Location-Based Social Networks (LBSN)(eg, Facebook, etc.) have many attributes (eg,
ratings, reviews, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations …

This work is the first step towards understanding when and if it is necessary for an Intelligent
Tutoring System (ITS) to explain its underlying user modeling techniques to students. We …

The text of a review expresses the sentiment a customer has towards a particular product.
This is exploited in sentiment analysis where machine learning models are used to predict …

In this paper we propose a new method of recommending not only items of interest to the
user but also the conditions enhancing user experiences with those items, such as …

In our increasingly algorithmic world, it is becoming more important, even compulsory, to
support automated decisions with authentic and meaningful explanations. We extend recent …

Most studies on recommender systems focus on collaborative algorithm approaches over
content‐based recommendation due to their better accuracy results. However, the …

The Location-Based Social Networks (LBSN)(eg, Facebook) have many factors (for
instance, ratings, check-in time, etc.) that play a crucial role for the Point-of-Interest (POI) …

The increasing volume of information has created overwhelming challenges to extract the
relevant items manually. Fortunately, the online systems, such as e-commerce (eg …