Explaining automatically generated recommendations allows users to make more informed
and accurate decisions about which results to utilize, and therefore improves their …

Collaborative Filtering (CF)-based recommendation algorithms, such as Latent Factor
Models (LFM), work well in terms of prediction accuracy. However, the latent features make it …

Explainable Recommendations provide the reasons behind why an item is recommended to
a user, which often leads to increased user satisfaction and persuasiveness. An intuitive way …

Recently, research on explainable recommender systems has drawn much attention from
both academia and industry, resulting in a variety of explainable models. As a consequence …

In modern recommender systems, there are usually comments or reviews from users that
justify their ratings for different items. Trained on such textual corpus, explainable …

A recommendation is called explainable if it not only predicts a numerical rating for an item,
but also generates explanations for users' preferences. Most existing methods for …

We describe a novel, multi-task recommendation model, which jointly learns to perform
rating prediction and recommendation explanation by combining matrix factorization, for …

Providing personalized explanations for recommendations can help users to understand the
underlying insight of the recommendation results, which is helpful to the effectiveness …

Recommender systems have been playing an increasingly important role in our daily life
due to the explosive growth of information. Accuracy and explainability are two core aspects …

Despite widespread adoption, recommender systems remain mostly black boxes. Recently,
providing explanations about why items are recommended has attracted increasing …