Time-Aware Collaborative Filtering for QoS-Based Service Recommendation

In QoS-based Web service recommendation, predicting QoS(Quality of Service) for service users will greatly aid service selection and discovery. In order to improve the prediction accuracy of Collaborative filtering algorithms, various factors are taken into account (e.g., location factor, environment, etc.). But seldom do investigators take the factor of time into account. Actually, QoS performance of Web services is highly related to the service status and network environments which are variable against time. Thus, this paper proposes a time-aware collaborative filtering algorithm to predict the missing QoS values. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the time-aware collaborative filtering algorithm significantly improves prediction accuracy.

[1]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[2]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[3]  Zhang Bin,et al.  A Web Service QoS Prediction Approach Based on Collaborative Filtering , 2010, 2010 IEEE Asia-Pacific Services Computing Conference.

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

[5]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

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

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

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

[9]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[10]  Luo Si,et al.  An automatic weighting scheme for collaborative filtering , 2004, SIGIR '04.

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

[12]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.

[13]  Harald C. Gall,et al.  Putting It All Together: Using Socio-technical Networks to Predict Failures , 2009, 2009 20th International Symposium on Software Reliability Engineering.

[14]  Maude Manouvrier,et al.  TQoS: Transactional and QoS-Aware Selection Algorithm for Automatic Web Service Composition , 2010, IEEE Transactions on Services Computing.

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

[16]  Xi Chen,et al.  RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation , 2010, 2010 IEEE International Conference on Web Services.

[17]  Elaine Rich,et al.  User Modeling via Stereotypes , 1998, Cogn. Sci..

[18]  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.

[19]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[20]  Zibin Zheng,et al.  Collaborative reliability prediction of service-oriented systems , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

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

[22]  Mingdong Tang,et al.  An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering , 2011, 2011 IEEE International Conference on Web Services.

[23]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[24]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.