The load prediction of host resources is a key issue to enhance the cloud computing aid allocation system. With the change of cloud computing resource load displaying extra and extra complicated characteristics, traditional prediction algorithms can solely predict the linear traits of data, and it is tough to precisely predict useful resource usage. In order to enhance the forecasting accuracy of the model, a blended load forecasting algorithm based totally on machine learning is proposed. The machine learning prediction model can nicely match the nonlinear traits of the data. The linear phase of the algorithm makes use of ARIMA prediction, and the nonlinear section makes use of particle swarm optimization algorithm to optimize LSTM prediction. Then, the optimal least squares method is used to redistribute the prediction error weights of the autoregressive differential moving average model (ARIMA) and the long-term and short-term memory network models (LSTM), and finally the prediction results are output. The comparison experiment is carried out with the open actual load data set. The experimental results show that the prediction accuracy of the weight redistribution combination model is significantly higher than that of other traditional prediction models and machine learning prediction models when the prediction time efficiency is similar, and the real-time prediction error of resource load in cloud environment is significantly reduced.
As the world's most valuable cryptocurrency, Bitcoin offers a new opportunity for price forecasting because of its high volatility, which is much higher compared to traditional currencies. Since bitcoin prices fluctuate randomly over time, we can use a time series model to predict the price of bitcoin. For this purpose, we use the ARIMA model to predict the future bitcoin price based on past prices. The basic idea of the ARIMA model is that the data series of the predicted object over time is considered as a random series, and some mathematical model is used to approximate this series. Once this model is determined, it is possible to predict the future values from the past values of the time series as well as the present values. The model achieves high accuracy and robustness. The result shows that there's inevitable deviation every time the price trend is having acute change, and the deviation of actual value to predicted one is positively correlated to the average value.
This paper considers a new type of two-sided matching in which multiple numbers of agents are perfectly matched on both sides. Such matching can be used between multiple major students and laboratories. The many-to-many perfect matching problem cannot be solved by existing many-to-many matching algorithms, since the perfect property, which is a global property, cannot be represented by the participants’ preferences, which are local properties. This paper gives a DA(Deferred Acceptance) mechanism to match each student to the given number of different laboratories without a blocking pair by introducing a master list of students to resolve ties between students.
In recent years, the recommendation field has gradually started to combine GNN-like approaches to address the challenges. The Neural Graph Collaborative Filtering (NGCF) framework has made a preliminary attempt to extract structural knowledge in model-based collaborative filtering based on graph convolution with message passing mechanisms, opening up new research possibilities. However, the NGCF framework does not consider the semantic information in the topology and only constructs a single heterogeneous graph. In our work, we suggest explicit semantic encoding of edges for different user behaviors and propose a Heterogeneous Graph Convolution Collaborative Filtering (HGCCF) framework combined with message propagation mechanism, which can mine richer collaborative information and effectively alleviate the sparsity problem of bipartite graph and enhance the cold start capability. Furthermore, we reduce the computational effort through compressing the initial embedding vector and sharing parameters in the message passing. Our Top-N recommendation experiments on pre-processed real e-commerce data from Alibaba verify that HGCCF has higher recommendation accuracy and the ability to cope with cold starts. In addition, we also design hyperparametric experiments of HGCCF to explore the effect of HGCCF on performance with different propagation learning layers, different normalization coefficients prui, and different output dimensions of embedding propagation layers.
In recent years, event-based social networks have developed rapidly, and event recommendation has attracted more and more attention. At present, for event recommendation, it is centered on the event, and aims to help users get the events which they are interested in from a large number of events. However, compared with traditional recommendation problems, event recommendation has many challenges. First of all, there is no obvious explicit rating of users’ response to events, but implicit feedback. Secondly, the recommendation of events has heterogeneous social network relations. At the same time, most users participate in few events, which leads to a very serious data sparsity problem. In order to address these challenges and improve the effectiveness of event recommendation, this paper proposes an event recommendation model that integrates users’ online and offline heterogeneous social network information and time information. The model uses Bayesian personalized ranking as the framework to process the implicit feedback information of users and events, and simultaneously combines online and offline social network information and time information to model together to improve the accuracy of recommendation. Experimental results based on real data sets show that the performance of the proposed model is better than other methods.
