Neural Explainable Collective Non-negative Matrix Factorization for Recommender Systems

Explainable recommender systems aim to generate explanations for users according to their predicted scores, the user’s history and their similarity to other users. Recently, researchers have proposed explainable recommender models using topic models and sentiment analysis methods providing explanations based on user’s reviews. However, such methods have neglected improvements in natural language processing, even if these methods are known to improve user satisfaction. In this paper, we propose a neural explainable collective nonnegative matrix factorization (NECoNMF) to predict ratings based on users’ feedback, for example, ratings and reviews. To do so, we use collective non-negative matrix factorization to predict user preferences according to different features and a natural language model to explain the prediction. Empirical experiments were conducted in two datasets, showing the model’s efficiency for predicting ratings and generating explanations. The results present that NECoNM F improves the accuracy for explainable recommendations in comparison with the state-of-art method in 18.3% for NDCG@5, 12.2% for HitRatio@5, 17.1% for NDCG@10, and 12.2% for HitRatio@10 in the Yelp dataset. A similar performance has been observed in the Amazon dataset 7.6% for NDCG@5, 1.3% for HitRatio@5, 7.9% for NDCG@10, and 3.9% for HitRatio@10.