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 …

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 …

In this paper we present a methodology to justify the suggestions generated by a
recommendation algorithm through the identification of relevant and distinguishing …

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 …

Incorporating review information into the recommender system has been demonstrated to be
an effective method for boosting the recommendation performance. Previous research …

Recommendation and review sites offer a wealth of information beyond ratings. For
instance, on IMDb users leave reviews, commenting on different aspects of a movie (eg …

Online reviews play a vital role in the decision-making process for online users. Helpful
reviews are usually buried in a large number of unhelpful reviews, and with the consistently …

Online social networking sites have become popular platforms on which users can link with
each other and share information, not only basic rating information but also information such …

Latent factor models have achieved great success in personalized recommendations, but
they are also notoriously difficult to explain. In this work, we integrate regression trees to …