NDLI: How Does Neighborhood Matter? An Empirical Study on MF Based QoS Prediction Framework (Short Paper)

Content Provider	IEEE Xplore Digital Library
Author	Wei Lo Jianwei Yin
Copyright Year	2013
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China (Wei Lo; Jianwei Yin)
Abstract	Quality-of-Service (QoS) is a fundamental element in Service-Oriented Computing (SOC) domain. Sufficient and accurate QoS information benefits to the parts not just in-depth analysis in theory but also in system deployments in practice. Studies of predicting missing QoS values is gaining important as an essential step to fast support operations in both academia and industry. Although there are a number of ongoing research works concerning how to build up Matrix Factorization (MF) based QoS prediction frameworks, it is usually not so easy for researchers and architects to adopt these tools, since there is a paucity of research literature on the general construction methodology and significant factors affecting performance. In this paper, we provide lessons by empirically examining how the neighborhood effect, the key concept of Collaborative Filtering (CF), matters to a robust MF based QoS engine. This work first proposes a general construction methodology to integrate neighborhood factor, and then evaluates significant elements matter to the final accuracy. In doing so, practitioners can clearly follow distilled guidelines for the successful implementation of neighborhood integrated QoS prediction frameworks, and make extensions for further purposes.
Starting Page	246
Ending Page	250
File Size	265904
Page Count	5
File Format	PDF
e-ISBN	9781479927029
DOI	10.1109/SOCA.2013.59
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Collaborative Filtering Web services Collaboration Training data Quality of service Linear programming Empirical Study Matrix Factorization QoS Prediction Manganese
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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