Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

Singh, Vijendra; Pandey, Manish Kumar; Singh, Pangambam Sendash; Karthikeyan, S.

doi:10.1016/j.procs.2020.04.140

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…Neural net time series for time-aware web services recommendation [77]; Deep collaborative filtering for web-service recommendations [78].…”

Section: Application Web-basedmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

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In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains.To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification. We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems. INDEX TERMSDeep learning, term classification, recommendation system, systematic review, state-ofthe-art techniques I. INTRODUCTION

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“…Neural net time series for time-aware web services recommendation [77]; Deep collaborative filtering for web-service recommendations [78].…”

Section: Application Web-basedmentioning

confidence: 99%

Deep Learning-Based Recommendation System: Systematic Review and Classification

Li,

Ishak,

Ibrahim

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…These systems make use of a variety of methods and variables. 15 Subsequently, web service instances can be classified into two categories, namely trusted and untrusted from users.…”

Section: Web Service Recommendationmentioning

confidence: 99%

“…A few endeavors have been done to foster a help revelation framework, for example, a web administration web crawler, web administration suggestion framework and web administration classifier. These systems make use of a variety of methods and variables 15 . Subsequently, web service instances can be classified into two categories, namely trusted and untrusted from users.…”

Section: Introductionmentioning

confidence: 99%

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.

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“…This algorithm was built on conjugate directions and not on a line-search. The stopping criteria are either a maximum number of epochs or the maximum amount of time exceeds or the performance reaches below to minimum gradient [ 46 , 47 ].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar

Alam

Ahmed

et al. 2021

Computational Intelligence and Neuroscience

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The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.

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Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation

Cited by 14 publications

References 25 publications

Deep Learning-Based Recommendation System: Systematic Review and Classification

Deep Learning-Based Recommendation System: Systematic Review and Classification

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Risk Prediction by Using Artificial Neural Network in Global Software Development

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