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It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users' interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user's score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy.

High-power AlGaN-based 385 nm near-ultraviolet light-emitting diodes (UVA-LEDs) grown on Si(111) substrates are reported. The threading dislocation (TD) density of AlGaN was reduced by employing an Al-composition step-graded AlN/AlGaN multilayer buffer. V-shaped pits were intentionally incorporated into the active region to screen the carriers from the nonradiative recombination centers (NRCs) around the TDs and to facilitate hole injection. The light extraction efficiency was enhanced by the surface roughening of a thin-film (TF) vertical chip structure. The as-fabricated TF-UVA-LED exhibited a light output power of 960 mW at 500 mA, corresponding to an external quantum efficiency of 59.7%. [ABSTRACT FROM AUTHOR]

Growth conditions were explored to suppress the carbon impurity incorporation in AlGaN:Mg grown at low temperatures. Electrical properties of Al0.07Ga0.93N:Mg samples with various carbon concentrations were investigated by Hall measurements. A clear correlation between carbon concentration and electrical conductivity has been found. By reducing the carbon concentration from 2 × 1018 to 5 × 1016 cm−3, the resistivity of p-Al0.07Ga0.93N decreases from 7.4 to 2.2 Ω·cm. From the results of the analysis of the charge neutrality equation, we found that the carbon concentration is close to the compensating donor concentration in the AlGaN:Mg samples, which suggests that carbon acts as the main compensating donor in AlGaN:Mg. [ABSTRACT FROM AUTHOR]

Two-step growth was employed to grow GaN quantum barriers (QBs) in InGaN green LD structures. A cap layer was grown at the same temperature as an InGaN quantum well (QW), and the temperature was then raised by around 130 °C to grow GaN QBs. The effects of low-temperature-grown cap (LT-cap) layers on the optical properties and microstructures of green LD structures were investigated. It was found that the LT-cap layer with an optimal thickness can improve the luminescence homogeneity and suppress the thermal decomposition of InGaN QWs. C-plane ridge waveguide laser diodes lasing above 500 nm were realized. [ABSTRACT FROM AUTHOR]