WSRec: A Collaborative Filtering Based Web Service Recommender System

As the abundance of Web services on the World Wide Web increase,designing effective approaches for Web service selection and recommendation has become more and more important. In this paper, we present WSRec, a Web service recommender system, to attack this crucial problem. WSRec includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web service QoS value prediction. WSRec is implemented by Java language and deployed to the real-world environment. To study the prediction performance, A total of 21,197 public Web services are obtained from the Internet and a large-scale real-world experiment is conducted, where more than 1.5 millions test results are collected from 150 service users in different countries on 100 publicly available Web services located all over the world. The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches.

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

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

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

[4]  Benjamin M. Marlin,et al.  Modeling User Rating Profiles For Collaborative Filtering , 2003, NIPS.

[5]  David E. Culler,et al.  PlanetLab: an overlay testbed for broad-coverage services , 2003, CCRV.

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

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

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

[9]  Munindar P. Singh,et al.  Agent-based service selection , 2004, J. Web Semant..

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

[11]  Kenneth Karta,et al.  An Investigation on Personalized Collaborative Filtering for Web Service Selection , 2005 .

[12]  Vincenzo Grassi,et al.  Flow-Based Service Selection forWeb Service Composition Supporting Multiple QoS Classes , 2007, IEEE International Conference on Web Services (ICWS 2007).

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

[14]  Lei Li,et al.  A Bayesian network based Qos assessment model for web services , 2007, IEEE International Conference on Services Computing (SCC 2007).

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

[16]  Liang-Jie Zhang,et al.  Services Computing: Core Enabling Technology of the Modern Services Industry , 2007 .

[17]  Zibin Zheng,et al.  A QoS-Aware Middleware for Fault Tolerant Web Services , 2008, 2008 19th International Symposium on Software Reliability Engineering (ISSRE).

[18]  Zibin Zheng,et al.  A Distributed Replication Strategy Evaluation and Selection Framework for Fault Tolerant Web Services , 2008, 2008 IEEE International Conference on Web Services.

[19]  Zibin Zheng,et al.  WS-DREAM: A distributed reliability assessment Mechanism for Web Services , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[20]  Maude Manouvrier,et al.  QoS-Driven Selection of Web Services for Transactional Composition , 2008, 2008 IEEE International Conference on Web Services.