Social Collaborative Viewpoint Regression with Explainable Recommendations
Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, Maarten de Rijke · 2017
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 explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction.