VAR (Vector Auto-regressive) model is a kind of commonly used econometric-model. It is used to estimate the dynamic relationship of the endogenous variables without any prior constraints. Since VAR is one of the most ...
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ISBN:
(纸本)9781538633496
VAR (Vector Auto-regressive) model is a kind of commonly used econometric-model. It is used to estimate the dynamic relationship of the endogenous variables without any prior constraints. Since VAR is one of the most easily operated models to deal with the analysis and prediction of multiple related economic indicators, more and more attention has been paid by economists in two decades. However, with the increasing of data size, the individual computer has encountered its processing bottleneck. Meanwhile, the advantages of the distributed computing cluster have begun to show obvious strength, such as Hadoop, Spark, and so on. Due to the lack of VAR related model on Spark, MLlib, we developed approaches of VAR and SVAR (Structural Vector Auto-regression) model in Spark and Hadoop cluster. Meanwhile, SGD (Stochastic Gradient Descent) algorithm has been applied after the data processing. To verify the approaches, different sizes of data are used for model testing in different platform, including R and Spark cluster. According to the comparison of the response time of different data size in both platform, the experiment results have shown that the developed methods are simple and efficient in big data environment.
The recommender system's overall suggestion results may be erroneous and weakly robust due to the sparse interaction behavior between users and items in the system and the noise inherent in interaction samples. Th...
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ISBN:
(纸本)9798400708305
The recommender system's overall suggestion results may be erroneous and weakly robust due to the sparse interaction behavior between users and items in the system and the noise inherent in interaction samples. This research suggests a graph comparison learning recommendation method based on knowledge graph augmentation to address the aforementioned issues. First, a knowledge graph-based data improvement method is created to make use of the rich entity attribute data and help the recommendation system solve its data sparsity issue. In order to help the model better capture the crucial information contained in the nodes and to lessen the effect of noisy links between interaction samples on the quality of node representation, a graph self-attentive augmented network is also proposed. In order to test the algorithm's efficacy, experiments are carried out on three datasets: Yelp2018, Amazon-Book, and MIND. The results show that the recommendation algorithm is effective in enhancing recommendation accuracy in the case of small sample sizes and the presence of noisy data scenarios. The Recall metrics are improved by 8.59%, 7.08%, and 15.47%, respectively, compared with the state-of-the-art model.
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