CARM: Confidence-aware recommender model via review representation learning and historical rating behavior

Abstract The recommendation systems in the online platforms scenario often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage reviews information to construct an accurate user/item latent factor. To address this problem, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperform the state-of-the-art methods. The source Python code will be available upon request.