Triple Factorization-Like Symmetric NLF Models With Latent Item–Item Relationship

Undirected, high-dimension and sparse (SHiDS) networks which are often encountered in industrial applications usually contain numerous useful information; thus, it is practically important to develop effective collaborative filtering methods to rigidly extract the latent information. In order to improve the performance of nonnegative latent factor (NLF) models based on the double factorization while reducing the computation burden of NLF models in terms of the triple factorization (TF) technique, a novel symmetric NLF (SNLF) model, i.e., TF-like SNLF model, is proposed in this article. A latent factor (LF) matrix that reflects the relationship of items is introduced into the objective function so as to offer more freedom for solutions. Then, a fixed weighting matrix designed by sensitivity experiments is used to replace the aforementioned LF matrix dependent on the update, which greatly helps save the time cost without sacrificing any accuracy. Furthermore, the alteration direction (ADI) method is adopted to design the algorithm, which effectively helps reduce the occurrence of the local minimum while guaranteeing convergence. Finally, experimental results on four real industrial data sets demonstrate that the proposed TF-like SNLF model makes a good compromise between the model performance and the time efficiency.