Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Qi, Lianyong; Zhou, Zhili; Yu, Jiguo; Liu, Qi

doi:10.1587/transinf.2016edp7490

Cited by 36 publications

(23 citation statements)

References 19 publications

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“…Dynamic scheduling and offloading techniques have also been employed to get good performance in recommenders. 29,30 Fault-tolerant scheduling: Fault-tolerant scheduling has been investigated in grids, multiple processor systems, and general clouds. Little attention has been paid to fault-tolerant scheduling in mobile cloud environment.…”

Section: Related Workmentioning

confidence: 99%

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Meng

et al. 2019

Computational Intelligence

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Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.

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Section: Related Workmentioning

confidence: 99%

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Meng

et al. 2019

Computational Intelligence

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“…Matrix factorization (MF) is a way to fill missing values in the user‐item purchase frequency matrix A . Commodity feature vectors are obtained by the MF model.…”

Section: The Ecfar Algorithmmentioning

confidence: 99%

“…Due to the objective function of the optimization problem in each iteration of the ALS can be split into independent least squares sub-problems, the ALS is suitable for distributed solution from a computational point of view. Formulas (8) and (9) can be executed in parallel on different workers to implement distributed computing.…”

Section: Matrix Factorization Modelmentioning

confidence: 99%

“…We note that the Collaborative filtering (CF) technique is widely used in E‐commerce and it utilizes a set of ratings made by users on items to generate recommendations, in which items can be of any type. Besides, Matrix Factorization (MF) technique provides a way to decompose the user‐item rating matrix into two matrices that represent users and items, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative filtering and association rule mining‐based market basket recommendation on spark

Wang

Wen

Guo

et al. 2019

Concurrency and Computation

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Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.

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“…In [13], the authors suggested that a user can publish partial QoS data to the service community, so as to protect the remaining majority of QoS data. Similarly, in [14], the authors take the amount of published data as a tunable parameter and then transform the privacy-preservation problem into a multi-object optimization problem, so as to achieve a good tradeoff between data availability and data privacy.…”

Section: Related Workmentioning

confidence: 99%

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Meng

Zhang

et al. 2018

Sensors

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Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

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Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Cited by 36 publications

References 19 publications

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Collaborative filtering and association rule mining‐based market basket recommendation on spark

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

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