S2NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation

Abstract

Nonnegative matrix factorization (NMF), which is aimed at making all elements of the factorization nonnegative and achieving nonlinear dimensional reduction at the same time, is an effective method for solving recommendation system problems. However, in many real-world applications, most models learn recommendation models under the supervised learning paradigm. Since the recommendation performance of NMF models relies heavily on initialization, the user-item interaction information is often very sparse. In many cases, supervised information about the data is difficult to obtain, resulting in a large number of existing models for supervised learning being inapplicable. To address this problem, we propose an information self-supervised NMF model for recommendation. Specifically, this model is based on the matrix factorization idea and introduces a self-supervised learning mechanism based on the NMF model to enhance the sparse data information of sparse data, and an easily extensible self-supervised NMF model was proposed. Furthermore, a corresponding gradient descent optimization algorithm was proposed, and the complexity of the algorithm was analysed. A large number of experimental results show that the proposed S2NMF has better performance.