Search

In the neighborhood-based collaborative filtering algorithms, the calculation of user (item) similarity has an important effect on the result of prediction and recommendation. Most of traditional similarity measures only consider co- rated items, which can get the similarity results quickly. However, the sparse datasets will lower the recommendation accuracy. At present, most of the advanced collaborative filtering algorithms improve the recommendation accuracy by designing complex similarity measures, but often ignore the computation cost in the model. A collaborative filtering algorithm combining similarity measure and pre-filtering mode is proposed in order to generate better recommendation in lower computation time. Firstly, the optimized similarity is obtained by defining the relative rating difference and enumerating the qualitative conditions that should be satisfied. At the same time, the rating preference based on the improved information entropy and the quantity information of user global ratings are considered as the two weight factors, which better distinguishes the differences between users and alleviates the problem of inaccurate similarity calculation under sparse data. Secondly, due to the inherent characteristics of the similarity measure and rating prediction formula, a pre-filtering model is proposed to filter out a large number of unnecessary users and the corresponding ratings, so as to further improve the computational efficiency. Finally, the collaborative filtering algorithm combining similarity measure and pre-filtering mode is obtained. Experimental results on the benchmark datasets indicate that the collaborative filtering algorithm has better recommendation quality and higher time efficiency than other eight comparison algorithms. [ABSTRACT FROM AUTHOR]

Using pine pollen as a new biological template, Ce-doped TiO2 porous microspheres were prepared via the sol–gel method and impregnation method after high-temperature calcination. The samples were characterized using scanning electron microscopy, X-ray energy spectrum analysis, X-ray diffraction, UV–Vis diffuse reflectance spectroscopy, photoluminescence spectroscopy, electrochemistry, and electron spin resonance spectroscopy. Owing to the doping of cerium, the bionic material showed a better photocatalytic antibacterial performance, and the antibacterial rate against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis surpassed 99.9% within 120 min under xenon lamp irradiation. In addition, we discuss the photocatalytic sterilization mechanism of porous Ce-doped TiO2 microspheres. The antibacterial activities of this biomimetic material are mainly due to the reactive oxygen species produced by its photocatalysis; these species attack bacteria, causing them to cleave and die. Inspired by natural pollen, the materials were prepared, and they have broad application prospects for photocatalytic disinfection. [ABSTRACT FROM AUTHOR]

Exploring the mechanism of self-assembly and guest encapsulation of nanocapsules is highly imperative for the design of sophisticated molecular containers and multistimuli-responsive functional materials. Here we present a molecular dynamics simulation protocol with implicit solvent and simulated annealing techniques to investigate the self-assembly and competitive guest (C 60 and C 70 fullerenes) encapsulation of a M 2 L 4 nanocapsule that is self-assembled by the coordination of mercury cations and bent bidentate ligands. Stepwise formation of the nanocapsule and competitive fullerene encapsulation during dynamic structural changes in the self-assembly were detected successfully. Such processes were driven by coordination bonding and π-π stacking and obey the minimum total potential energy principle. Potential of mean force calculations for guest binding to the M 2 L 4 nanocapsule explained the mechanism underlying the competitive encapsulations of C 60 and C 70 . This work helps design new functional nanomaterials capable of guest encapsulation and release.