NDLI: Web Service Recommendation Based on Time Series Forecasting and Collaborative Filtering

Content Provider	IEEE Xplore Digital Library
Author	Yan Hu Qimin Peng Xiaohui Hu Rong Yang
Copyright Year	2015
Description	Author affiliation: Sci. & Technol. of Integrated Inf. Syst. Lab., Inst. of Software, Beijing, China (Yan Hu; Qimin Peng; Xiaohui Hu) \|\| Coll. of Comput. Sci. & Technol., Hubei Univ. of Sci. & Technol., Xianning, China (Rong Yang)
Abstract	Quality of Service (QoS) has been widely used for personalized Web service recommendation. Since QoS information usually cannot be predetermined, how to make personalized QoS prediction precisely becomes a challenge of Web service recommendation. Time series forecasting and collaborative filtering are two mainstream technologies for QoS prediction. However, on one hand, existing time series forecasting approaches based on Auto Regressive Integrated Moving Average (ARIMA) models do not take the latest observation as a feedback to revise forecasts. Moreover, they only focus on predicting future QoS values for each individual Web service. Service users' personalized factors are not taken into account. On the other hand, collaborative filtering facilitates user-side personalized QoS evaluation, but rarely precisely models the temporal dynamics of QoS values. To address the limitations of existing QoS prediction methods, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the personalized factors of service users. Our approach seamlessly combines collaborative filtering with improved time series forecasting which uses Kalman filtering to compensate for shortcomings of ARIMA models. Finally, the experimental results show that the proposed approach can improve the accuracy of personalized QoS prediction significantly.
Starting Page	233
Ending Page	240
File Size	816303
Page Count	8
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quality of service Web services Predictive models Time series analysis Forecasting Kalman filters Computational modeling collaborative filtering Web service recommendation QoS prediction ARIMA Kalman filtering
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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