Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach

Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous social networks through the user-item bipartite graph, user social network graph and user-attribute bipartite graph; and then uses grey relational analysis to identify implicit social relationships, which are then incorporated into the matrix factorization model. Five experiments were conducted to test the performance of our approach against four state-of-the-art baseline methods. The results show that compared with the baseline methods, our approach can effectively alleviate the sparsity problem, because the heterogeneous social network provides richer information. In addition, the grey relational analysis method has the advantage of low requirements for data size and efficiently relieves the cold start problem. Furthermore, our approach saves processing time, thus increases recommendation efficiency. Overall, the proposed approach can effectively improve the accuracy of rating prediction in social recommendations and provide accurate and efficient recommendation service for users

Numerical Study of the Spin Hall Conductance in the Luttinger Model

We present first numerical studies of the disorder effect on the recently proposed intrinsic spin Hall conductance in a three dimensional (3D) lattice Luttinger model. The results show that the spin Hall conductance remains finite in a wide range of disorder strength, with large fluctuations. The disorder-configuration-averaged spin Hall conductance monotonically decreases with the increase of disorder strength and vanishes before the Anderson localization takes place. The finite-size effect is also discussed.Comment: 4 pages, 4 figures; the final version appearing in PR

Second Renormalization of Tensor-Network States

We propose a second renormalization group method to handle the tensor-network states or models. This method reduces dramatically the truncation error of the tensor renormalization group. It allows physical quantities of classical tensor-network models or tensor-network ground states of quantum systems to be accurately and efficiently determined.Comment: 5 figures, 4 page

Determinación de la calidad del aceite de té mediante 19F RMN y 1H RMN

The nuclear magnetic resonance (NMR) technique was applied to monitor the quality of tea oil herein. The adulteration of virgin tea oil was monitored by 19F NMR and 1H NMR. The 19F NMR technique was used as a new method to detect the changes in quality and hydroperoxide value of tea oil. The research demonstrates that 19F NMR and 1H NMR can quickly detect adulteration in tea oil. High temperature caused a decrease in the ratio D and increase in the total diglyceride content. Some new peaks belonging to the derivatives of hydroperoxides appeared at δ-108.21 and δ-109.05 ppm on the 19F NMR spectrum when the oil was autoxidized and became larger when the hydroperoxide value increased. These results have great significance in monitoring the moisture content, freshness and oxidation status of oils and in detecting adulteration in high priced edible oils by mixing with cheap oils.En este trabajo se utiliza la técnica de resonancia magnética nuclear (RMN) para controlar la calidad del aceite de té. La adulteración del aceite de té virgen se controló mediante las técnicas de 19F RMN y 1H RMN. La técnica de 19F RMN se utilizó como un nuevo método para detectar los cambios en la calidad y el índice de hidroperóxido del aceite de té. La investigación demuestra que las técnicas 19F RMN y 1H RMN pueden detectar rápidamente la adulteración del aceite de té. La alta temperatura provoca una disminución en la proporción D y un aumento en el contenido total de diglicéridos. Algunos picos nuevos, pertenecientes a derivados de hidroperóxidos, aparecieron a δ-108,21 y δ-109,05 ppm en el espectro de 19F RMN cuando el aceite se autoxidaba e incrementaban cuando aumentaba el índice de hidroperóxido. Estos resultados tienen gran importancia en el seguimiento del contenido de humedad, de la frescura y del estado de oxidación de los aceites y en la detección de la adulteración de aceites comestibles de alto valor con aceites baratos mediante el uso de 19F RMN y 1H RMN