Services Computing – SCC 2018

The existing content-based recommendation techniques can’t provide personalized recommendation for each user while mining the potential interest of them. In order to solve this problem, we analyze the viewing behavior of users, the attribute of programs and the association of tags to establish the user-tag model, program-tag model and tag-tag model. To realize the improved algorithm 1, the relationship among user, program and tag is reasonably used. In consideration of related interest, the original interest is also taken into account to realize the improved algorithm 2. After the optimization, accuracy and recall rate increase by 0.41% and 0.49% respectively.

[1]  Herman Arnold Engelbrecht,et al.  Forecasting methods for cloud hosted resources, a comparison , 2015, 2015 11th International Conference on Network and Service Management (CNSM).

[2]  Schahram Dustdar,et al.  Data-driven and automated prediction of service level agreement violations in service compositions , 2013, Distributed and Parallel Databases.

[3]  Mir Ali Seyyedi,et al.  Qos monitoring for web services by Time Series Forecasting , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[4]  Dimitris Plexousakis,et al.  Requirements for QoS-Based Web Service Description and Discovery , 2009, IEEE Trans. Serv. Comput..

[5]  Jeffrey M. Stanton,et al.  Analysis of end user security behaviors , 2005, Comput. Secur..

[6]  Lujo Bauer,et al.  Encountering stronger password requirements: user attitudes and behaviors , 2010, SOUPS.

[7]  Zibin Zheng,et al.  Investigating QoS of Real-World Web Services , 2014, IEEE Transactions on Services Computing.

[8]  Qingsheng Zhu,et al.  Dependability Prediction of WS-BPEL Service Compositions Using Petri Net and Time Series Models , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[9]  Markus Strohmaier,et al.  When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks , 2012, #MSM.

[10]  Lars Grunske,et al.  An automated approach to forecasting QoS attributes based on linear and non-linear time series modeling , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.

[11]  Zibin Zheng,et al.  Personalized Reliability Prediction of Web Services , 2013, TSEM.

[12]  Jinpeng Huai,et al.  An Adaptive Web Services Selection Method Based on the QoS Prediction Mechanism , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[13]  Farookh Khadeer Hussain,et al.  Time Series QoS Forecasting for Management of Cloud Services , 2014, 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications.

[14]  Hai Dong,et al.  Long-Term QoS-Aware Cloud Service Composition Using Multivariate Time Series Analysis , 2016, IEEE Transactions on Services Computing.

[15]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[16]  Lars Grunske,et al.  An Approach to Forecasting QoS Attributes of Web Services Based on ARIMA and GARCH Models , 2012, 2012 IEEE 19th International Conference on Web Services.

[17]  Jong-Yih Kuo,et al.  Search based approach to forecasting QoS attributes of web services using genetic programming , 2016, Inf. Softw. Technol..

[18]  Gerardo Canfora,et al.  An empirical comparison of methods to support QoS-aware service selection , 2010, PESOS '10.

[19]  Zibin Zheng,et al.  WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services , 2011, 2011 IEEE 22nd International Symposium on Software Reliability Engineering.

[20]  Dan Boneh,et al.  Stronger Password Authentication Using Browser Extensions , 2005, USENIX Security Symposium.

[21]  Mohammad Kazem Akbari,et al.  Modeling and predicting measured response time of cloud-based web services using long-memory time series , 2015, The Journal of Supercomputing.

[22]  Umesh Bellur,et al.  Automating QoS Based Service Selection , 2010, 2010 IEEE International Conference on Web Services.

[23]  Jong-Yih Kuo,et al.  Time series forecasting for dynamic quality of web services: An empirical study , 2017, J. Syst. Softw..