scholarly journals Collaborative filtering recommendation algorithm in cloud computing environment

2021 ◽  
Vol 18 (2) ◽  
pp. 517-534
Author(s):  
Pei Tian
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Programming Model ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Computing Environment ◽  
Recommendation Algorithm ◽  
Cloud Computing Environment ◽  
Detection Technology ◽  
Application Data

With the advent of the era of cloud computing, the amount of application data increases dramatically, and personalized recommendation technology becomes more and more important. This paper mainly studies the collaborative filtering detection algorithm in the cloud computing environment. The algorithm migrates the collaborative filtering detection technology and applies it to the cloud computing environment. It shortens the recommendation time by using the advantages of clustering. A new recommendation algorithm can improve the accuracy of recommendation, and proposes a parallel collaborative filtering recommendation algorithm based on project. The algorithm is designed with programming model The experimental results show that the proposed algorithm has shorter running time and better scalability than the existing parallel algorithm.

Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

2021 ◽  
pp. 751-769
Author(s):  
Xinling Tang ◽  
Hongyan Xu ◽  
Yonghong Tan ◽  
Yanjun Gong
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Distributed Storage ◽  
Distributed Processing ◽  
Storage System ◽  
Personalized Recommendation ◽  
Computing Environment ◽  
Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

E-Commerce Collaborative Filtering Algorithm Research Based on Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1566-1569
Author(s):  
Wen Qing Zhao ◽  
Fei Fei Han ◽  
Rui Cai ◽  
De Wen Wang
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Online Shopping ◽  
Programming Model ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Consumption Data ◽  
Parallel Programming Model ◽  
Collaborative Filtering Algorithm

With the continuous development of online shopping, a day will generate tens of thousands of consumer records. E-commerce sites want to recommend the consumers that they may be interested in the products by analyzing the consumer historical consumption data. However massive consumer records led to recommendation speed getting slow by using the traditional personalized recommendation algorithm. By researching on the collaborative filtering algorithm based on ALS and the MapReduce parallel programming model, we explore parallelization of collaborative filtering algorithm based on ALS. The experimental results show that the algorithm in this paper can improve the computing efficiency.

Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

2017 ◽  
Vol 12 (3) ◽  
pp. 11-27 ◽  
Author(s):  
Xinling Tang ◽  
Hongyan Xu ◽  
Yonghong Tan ◽  
Yanjun Gong
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Distributed Storage ◽  
Distributed Processing ◽  
Storage System ◽  
Personalized Recommendation ◽  
Computing Environment ◽  
Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

scholarly journals Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery

2014 ◽  
Vol XL-1 ◽  
pp. 27-34
Author(s):  
L. M. Almutairi ◽  
S. Shetty ◽  
H. G. Momm
Keyword(s):  
Cloud Computing ◽  
Genetic Programming ◽  
Information Extraction ◽  
Evolutionary Computation ◽  
Programming Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Extraction Process ◽  
Computing Environment ◽  
Cloud Computing Environment

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

2016 ◽  
Vol 11 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Abdullah Alamareen ◽  
Omar Al-Jarrah ◽  
Inad A. Aljarrah
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Edge Detection ◽  
Processing Speed ◽  
Message Passing ◽  
Detection Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Edge Detection Algorithm ◽  
Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

scholarly journals Study of Immune-Based Intrusion Detection Technology in Virtual Machines for Cloud Computing Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Virtual Machine ◽  
Virtual Machines ◽  
The State ◽  
Signal Acquisition ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Model ◽  
Detection Technology

Cloud computing platforms are usually based on virtual machines as the underlying architecture; the security of virtual machine systems is the core of cloud computing security. This paper presents an immune-based intrusion detection model in virtual machines of cloud computing environment, denoted as IB-IDS, to ensure the safety of user-level applications in client virtual machines. In the model, system call sequences and their parameters of processes are used, and environment information in the client virtual machines is extracted. Then the model simulates immune responses to ensure the state of user-level programs, which can detect attacks on the dynamic runtime of applications and has high real-time performance. There are five modules in the model: antigen presenting module, signal acquisition module, immune response module, signal measurement module, and information monitoring module, which are distributed into different levels of virtual machine environment. Performance analysis and experimental results show that the model brings a small performance overhead for the virtual machine system and has a good detection performance. It is applicable to judge the state of user-level application in guest virtual machine, and it is feasible to use it to increase the user-level security in software services of cloud computing platform.

Image Mosaicing Using Binary Edge Detection Algorithm in a Cloud-Computing Environment

Fog Computing ◽  
2018 ◽  
pp. 183-197
Author(s):  
Abdullah Alamareen ◽  
Omar Al-Jarrah ◽  
Inad A. Aljarrah
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Edge Detection ◽  
Processing Speed ◽  
Message Passing ◽  
Detection Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Edge Detection Algorithm ◽  
Binary Edge

Image Mosaicing is an image processing technique that arises from the need of having a more realistic view of the real world wider than the view captured by the lenses of the available cameras. In this paper, a sequence of images will be mosaiced using binary edge detection algorithm in a cloud-computing environment to improve processing speed and accuracy. The authors have used Platform as a Service (PaaS) to provide a number of nodes in the cloud to run the computational intensive image processing and stitching algorithms. This increased the processing speed as most of image processing algorithms deal with every single pixel in the image. Message Passing Interface (MPI) is used for message passing among the compute-nodes in the cloud and a MapReduce technique is used for image distribution and collection, where the root node is used as reducer and the others as mappers. After applying the algorithm on different sequence of images and different machines on JUST cloud, the authors have achieved high mosaicing accuracy, and the execution time has been improved when comparing it with sequential execution on the images.

scholarly journals The application of e-commerce recommendation system in smart cities based on big data and cloud computing

2021 ◽  
pp. 26-26
Author(s):  
Yiman Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Evaluation System ◽  
Recommendation System ◽  
Smart Cities ◽  
Personalized Recommendation ◽  
Current Recommendation ◽  
Recommendation Algorithm

In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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