The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is...The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.展开更多
To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The c...To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The circuit model takes both semiconductor and electrostatic effects into account, and is valid for low and medium signal frequencies. The electrostatic capacitances are extracted with a floating random walk based algorithm, and are then combined with the voltage-dependent semiconductor capacitances to form the equivalent circuit. Compared with the method used in Synopsys＇s Sdevice, which completely simulates the electro/semiconductor effects, the proposed method is more efficient and is able to handle the general TSV layout as well. For several TSV structures, the experimental results validate the accuracy of the proposed method for the frequency range from l0 kHz to 1 GHz. The proposed method demonstrated 47× speedup over the Sdevice for the largest 9-TSV case.展开更多
文摘The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.
基金supported by the National Natural Science Foundation of China(No.61422402)the Tsinghua University Initiative Scientific Research Program
文摘To build an accurate electric model for through-silicon vias （TSVs） in 3D integrated circuits （ICs）, a resistance and capacitance （RC） circuit model and related efficient extraction technique are proposed. The circuit model takes both semiconductor and electrostatic effects into account, and is valid for low and medium signal frequencies. The electrostatic capacitances are extracted with a floating random walk based algorithm, and are then combined with the voltage-dependent semiconductor capacitances to form the equivalent circuit. Compared with the method used in Synopsys＇s Sdevice, which completely simulates the electro/semiconductor effects, the proposed method is more efficient and is able to handle the general TSV layout as well. For several TSV structures, the experimental results validate the accuracy of the proposed method for the frequency range from l0 kHz to 1 GHz. The proposed method demonstrated 47× speedup over the Sdevice for the largest 9-TSV case.
