A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation

Due to the popularity of service-oriented architectures for various distributed systems, an increasing number of Web services have been deployed all over the world. Recently, Web service recommendation became a hot research topic, one that aims to accurately predict the quality of functional satisfactory services for each end user. Generally, the performance of Web service changes over time due to variations of service status and network conditions. Instead of employing the conventional temporal models, we propose a novel spatial-temporal QoS prediction approach for time-aware Web service recommendation, where a sparse representation is employed to model QoS variations. Specifically, we make a zero-mean Laplace prior distribution assumption on the residuals of the QoS prediction, which corresponds to a Lasso regression problem. To effectively select the nearest neighbor for the sparse representation of temporal QoS values, the geo-location of web service is employed to reduce searching range while improving prediction accuracy. The extensive experimental results demonstrate that the proposed approach outperforms state-of-art methods with more than 10% improvement on the accuracy of temporal QoS prediction for time-aware Web service recommendation.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[2]  Zibin Zheng,et al.  WSRec: A Collaborative Filtering Based Web Service Recommender System , 2009, 2009 IEEE International Conference on Web Services.

[3]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[4]  Kecheng Liu,et al.  Personalized Web Service Ranking via User Group Combining Association Rule , 2009, 2009 IEEE International Conference on Web Services.

[5]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[6]  Jianke Zhu,et al.  Network-Aware QoS Prediction for Service Composition Using Geolocation , 2015, IEEE Transactions on Services Computing.

[7]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

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

[9]  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.

[10]  Songjie Gong.  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

[11]  Hui Lei,et al.  Event-Driven Quality of Service Prediction , 2008, ICSOC.

[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]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[14]  Bin Zhang,et al.  Performance Prediction Based EX-QoS Driven Approach for Adaptive Service Composition , 2009, J. Inf. Sci. Eng..

[15]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[16]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[17]  Daniel A. Menascé,et al.  QoS Issues in Web Services , 2002, IEEE Internet Comput..

[18]  Freddy Lécué,et al.  Using Context Similarity for Service Recommendation , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[19]  Paul J. Kennedy,et al.  HDAX: Historical Symbolic Modelling of Delay Time Series in a Communications Network , 2009, AusDM.

[20]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[21]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[22]  Zibin Zheng,et al.  Personalized QoS-Aware Web Service Recommendation and Visualization , 2013, IEEE Transactions on Services Computing.

[23]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[24]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[25]  Gero Mühl,et al.  QoS aggregation for Web service composition using workflow patterns , 2004, Proceedings. Eighth IEEE International Enterprise Distributed Object Computing Conference, 2004. EDOC 2004..

[26]  Matthew Richardson,et al.  Yes, there is a correlation: - from social networks to personal behavior on the web , 2008, WWW.

[27]  Liang Cai,et al.  Geographic Location-Based Network-Aware QoS Prediction for Service Composition , 2013, 2013 IEEE 20th International Conference on Web Services.

[28]  Fuyuki Ishikawa,et al.  Towards network-aware service composition in the cloud , 2012, WWW.

[29]  Zhaohui Wu,et al.  Collaborative Web Service QoS Prediction with Location-Based Regularization , 2012, 2012 IEEE 19th International Conference on Web Services.

[30]  Xiongcai Cai,et al.  Learning Collaborative Filtering and Its Application to People to People Recommendation in Social Networks , 2010, 2010 IEEE International Conference on Data Mining.

[31]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[32]  Zibin Zheng,et al.  Trace Norm Regularized Matrix Factorization for Service Recommendation , 2013, 2013 IEEE 20th International Conference on Web Services.

[33]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

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

[35]  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.

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

[37]  K. Hadri.  Testing The Null Hypothesis Of Stationarity Against The Alternative Of A Unit Root In Panel Data With Serially Correlated Errors , 1999 .

[38]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[39]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[40]  Mingdong Tang,et al.  Location-Aware Collaborative Filtering for QoS-Based Service Recommendation , 2012, 2012 IEEE 19th International Conference on Web Services.

[41]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[42]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[43]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[44]  Zibin Zheng,et al.  Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering , 2013, IEEE Transactions on Services Computing.

[45]  Edward I. George,et al.  A bayesian model for collaborative filtering , 1999, AISTATS.

[46]  Edward Y. Chang,et al.  Collaborative filtering for orkut communities: discovery of user latent behavior , 2009, WWW '09.

[47]  Lina Yao,et al.  Recommending Web Services via Combining Collaborative Filtering with Content-Based Features , 2013, 2013 IEEE 20th International Conference on Web Services.