Baogui Xin

Yanqin Liu, Fawang Liu, Libo Feng et al.

In this paper, we consider the finite difference method for the generalized two-dimensional (2D) multi-term time-fractional Oldroyd-B fluid model, which is a subclass of non-Newtonian fluids. Different from the general multi-term time fractional equations, the generalized fluid equation not only has a multi-term time derivative but also possess a special time fractional operator on the spatial derivative. Firstly, a new discretization of the time fractional derivative is given. And a vital lemma, which plays an important role in the proof of stability, is firstly proposed. Then the new finite difference scheme is constructed. Next, the unique solvability, unconditional stability, and convergence of the proposed scheme are proved by the energy method. Numerical examples are given to verify the numerical accuracy and efficiency of the numerical scheme as compared to theoretical analysis, and this numerical method can be extended to solve other non-Newtonian fluid models.

Baogui Xin, Wei Peng

We propose a novel filter for sparse big data, called an integrated autoencoder (IAE), which utilizes auxiliary information to mitigate data sparsity. The proposed model achieves an appropriate balance between prediction accuracy, convergence speed, and complexity. We implement experiments on a GPS trajectory dataset, and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.

Wei Peng, Baogui Xin

The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF) recommendation system is proposed to solve the above-mentioned problems. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly higher than that of some state-of-the-art recommendation algorithms. The proposed SPMF algorithm is a more accurate and effective recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.