In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new init...In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new initial GM（1, 1） model is not ideal by the concrete example. Then according to the principle of making the sum of the squares of the difference between the calculated sequences and the original sequences, an optimized GM（1, I） model was established. The result shows that this method is a new prediction method which can predict the maximum water inflow accurately. It not only conforms to the guide- line of prevention primarily, but also provides reference standards to managers on making prevention measures.展开更多
Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning probl...Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.展开更多
文摘In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new initial GM（1, 1） model is not ideal by the concrete example. Then according to the principle of making the sum of the squares of the difference between the calculated sequences and the original sequences, an optimized GM（1, I） model was established. The result shows that this method is a new prediction method which can predict the maximum water inflow accurately. It not only conforms to the guide- line of prevention primarily, but also provides reference standards to managers on making prevention measures.
基金Project supported by the National Natural Science Foundation of China (Nos. 60525108 and 60533090)the National Hi-Tech Research and Development Program (863) of China (No. 2006AA010107)the Program for Changjiang Scholars and Innovative Research Team in University, China (No. IRT0652)
文摘Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.
