Our paper titled “Personalized Novel and Explainable Matrix Factorization” is accepted in DKE journal from Elsevier. The abstract of the paper is the following:
A recommendation system personalises suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, except of being accurate, should be also novel and explainable. However, up to now most platforms fail to provide both novel recommendations that advance users’ exploration along with explanations to make their reasoning more transparent to them. For example, a well-known recommendation algorithm, such as matrix factorisation (MF), tries to optimise only the accuracy criterion, while disregards other quality criteria such as the explainability, but also the novelty, of recommended items. In this paper, to the best of our knowledge, we propose a new model, denoted as NEMF, that allows to trade-off the MF performance with respect to the criteria of novelty and explainability, while only minimally compromising on accuracy. In addition, we recommend a new explainability metric based on nDCG, which distinguishes a more explainable item from a less explainable item. Our extensive experimental results demonstrate that we attain high accuracy by recommending also novel and explainable items. We have also explored with a user study how users perceive the different attributes (i.e., number of ratings, mean average, and origin of ratings) of the “user” style of explanation.