An Approach to Alleviate the Sparsity Problem of Hybrid Collaborative Filtering Based Recommendations: The Product-Attribute Perspective from User Reviews

Yang, Xiaoxian; Zhou, Sijing; Cao, Min

doi:10.1007/s11036-019-01246-2

Cited by 105 publications

(57 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though there are other scheduling strategies have been widely studied in the workflow scheduling problem [33][34][35][36][37][38], most of the existing scheduling algorithms focus on fixed resource allocation optimization whereas few can realize online scheduling. Meanwhile, almost all existing scheduling algorithms fail to completely incorporate all the characteristics of cloud, such as dynamic expansion, heterogeneity, VM performance deviation and bandwidth variation, or ignore the data transfer time between tasks of a workflow.…”

Section: Scheduling For Multiple Workflows In a Cloudmentioning

confidence: 99%

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

View full text Add to dashboard Cite

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

show abstract

Section: Scheduling For Multiple Workflows In a Cloudmentioning

confidence: 99%

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Gao

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It also points out that many machine learning algorithms can be introduced into the research of code completion. These are similar to the methods recommended for the code, and there are many in life [11] [27]. However, these methods have poor ability to deal with OoV problems, and some of them have not considered this issue.…”

Section: Related Workmentioning

confidence: 80%

Graph embedding code prediction model integrating semantic features

Yang

Fan

et al. 2020

COMSIS J

View full text Add to dashboard Cite

With the advent of Big Code, code prediction has received widespread attention. However, the state-of-the-art code prediction techniques are inadequate in terms of accuracy, interpretability and efficiency. Therefore, in this paper, we propose a graph embedding model that integrates code semantic features. The model extracts the structural paths between the nodes in source code file?s Abstract Syntax Tree(AST). Then, we convert paths into training graph and extracted interdependent semantic structural features from the context of AST. Semantic structure features can filter predicted candidate values and effectively solve the problem of Out-of- Word(OoV). The graph embedding model converts the structural features of nodes into vectors which facilitates quantitative calculations. Finally, the vector similarity of the nodes is used to complete the prediction tasks of TYPE and VALUE. Experimental results show that compared with the existing state-of-the-art method, our method has higher prediction accuracy and less time consumption.

show abstract

“…We implement the BR and CC algorithms using the open source multi-label classification toolkit Scikit-Multilearn [31], and use Support Vector Machine (SVM) as the basic classifier in these algorithms [32] [33]. Based on pre-trained vehicle domain word vectors, five typical multi-label classification methods are tested on two vehicle complaint datasets.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

VDRF: Sensing the defect information to risk level of vehicle recall based on bert communication model

You

Zhang

et al. 2020

COMSIS J

View full text Add to dashboard Cite

The recall of defective automobile products is one of the important measures to promote the quality of product quality and protect consumers' pyhsical safety and property security. In order to assess the risk level of defect cases, automobile recall management experts need to analyze and discuss the defect information by personal. A risk level prediction method based on language pre-training Bert model is proposed in this paper, which can transform the defect information into rick level of the vehicle and then predict vehicle recall automatically, in which a seq2seq model is proposed to multi-label the vehicle complaint data. The outputs of the seq2seq model combined with other static and dynamic information are used as the input of the Bert communication model. Substantial comparative experiments of different feature combinations on different methods show that the proposed VDRF method achieves F1 value with 79% in vehicle recall risk prediction, which outperforms the traditional method.

show abstract

An Approach to Alleviate the Sparsity Problem of Hybrid Collaborative Filtering Based Recommendations: The Product-Attribute Perspective from User Reviews

Cited by 105 publications

References 32 publications

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Graph embedding code prediction model integrating semantic features

VDRF: Sensing the defect information to risk level of vehicle recall based on bert communication model

Contact Info

Product

Resources

About