Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation Article Swipe
Vijendra Pratap Singh
,
Manish Kumar Pandey
,
Pangambam Sendash Singh
,
Karthikeyan Subbiah
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2020.04.140
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2020.04.140
The convergence of Social Mobility Analytics and Cloud (SMAC) technologies resulted in an unexpected upsurge of web services on the internet. The flexibility and rental approach of the cloud makes it an attractive option for the deployment of web services-based applications. Once a number of web services are available to gratify the similar functionalities, then the choice of the web service based on personalized quality of service (QoS) parameters plays an important role in deciding the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the correlated behavior of the non-functional performance of web services with the invocation time. This happens because service status usually changes over time. These limitations have affected the performance of neighborhood-based collaborative filtering. Hence, the design of the time aware web service recommendation system based on the personalized QoS parameters is very crucial and turns out to be a challenging research issue. In the current work, various neural network models like Levenberg Marquardt (LM), Bayesian-Regularization (BR) and Scaled-Conjugate-Gradient (SCG) are used for experimentation with the input time series to find the best fit model for the prediction of personalized QoS based web services recommendation. The Pearson's Correlation Coefficient is used as an evaluation metric and their value for the prediction of Response time is found to be 0.84985 and for throughput (TP) is found to be 0.99082 with the Levenberg Marquardt algorithm. Thus, the experimental results show that the with the Levenberg Marquardt model of Time Series Forecasting for Web Services Recommendation Framework is performing better in case of Response time as well as throughput.
Related Topics
Concepts
Computer science
Quality of service
Web service
Collaborative filtering
Cloud computing
The Internet
Web analytics
World Wide Web
Machine learning
Web application security
Recommender system
Computer network
Web development
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2020.04.140
- OA Status
- diamond
- Cited By
- 15
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3033088600
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3033088600Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2020.04.140Digital Object Identifier
- Title
-
Neural Net Time Series Forecasting Framework for Time-Aware Web Services RecommendationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Vijendra Pratap Singh, Manish Kumar Pandey, Pangambam Sendash Singh, Karthikeyan SubbiahList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2020.04.140Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2020.04.140Direct OA link when available
- Concepts
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Computer science, Quality of service, Web service, Collaborative filtering, Cloud computing, The Internet, Web analytics, World Wide Web, Machine learning, Web application security, Recommender system, Computer network, Web development, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 4, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2889417953, https://openalex.org/W6732446812, https://openalex.org/W2598519062, https://openalex.org/W6922530732, https://openalex.org/W2092513324, https://openalex.org/W2110618516, https://openalex.org/W2075422010, https://openalex.org/W2071424233, https://openalex.org/W2108444096, https://openalex.org/W2030239032, https://openalex.org/W2140597141, https://openalex.org/W2091381870, https://openalex.org/W2124085800, https://openalex.org/W2142144955, https://openalex.org/W6655354761, https://openalex.org/W2025495819, https://openalex.org/W2049455633, https://openalex.org/W1966553486, https://openalex.org/W2037111396, https://openalex.org/W2014318101, https://openalex.org/W2738639449, https://openalex.org/W2119462974, https://openalex.org/W1251444851, https://openalex.org/W2119264846, https://openalex.org/W2102599712, https://openalex.org/W2146356176, https://openalex.org/W1965810726, https://openalex.org/W2510603126, https://openalex.org/W1539645453, https://openalex.org/W1980321478, https://openalex.org/W2093063389, https://openalex.org/W6695098639, https://openalex.org/W2888617857, https://openalex.org/W2073178357, https://openalex.org/W3004212246, https://openalex.org/W6775728322, https://openalex.org/W2279773298, https://openalex.org/W3016427833, https://openalex.org/W2018597607, https://openalex.org/W2035424143, https://openalex.org/W4292483811, https://openalex.org/W2576367398 |
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