Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A Peer-to-Peer Approach to Web Service Discovery
World Wide Web
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Preference-based selection of highly configurable web services
Proceedings of the 16th international conference on World Wide Web
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Flexible Semantic-Based Service Matchmaking and Discovery
World Wide Web
WSRec: A Collaborative Filtering Based Web Service Recommender System
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Personalized Web Service Ranking via User Group Combining Association Rule
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Discovering Homogeneous Web Service Community in the User-Centric Web Environment
IEEE Transactions on Services Computing
Computing Service Skyline from Uncertain QoWS
IEEE Transactions on Services Computing
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Computing Service Skylines over Sets of Services
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
A user centric service-oriented modeling approach
World Wide Web
On the Use of Fuzzy Dominance for Computing Service Skyline Based on QoS
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Collaborative Filtering Based Service Ranking Using Invocation Histories
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Service selection algorithms for composing complex services with multiple qos constraints
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Multi-attribute optimization in service selection
World Wide Web
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We present in this paper a novel collaborative filtering based scheme for evaluating the QoS of large scale Web services. The proposed scheme automates the process of assessing the QoS of a priori unknown service providers and thus facilitates service users in selecting services that best match their QoS requirements. Most existing service selection approaches ignore the great diversity in the service environment and assume that different users receive identical QoS from the same service provider. This may lead to inappropriate selection decisions as the assumed QoS may deviate significantly from the one actually received by the users. The collaborative filtering based approach addresses this issue by taking the diversity into account instead of uniformly applying the same QoS value to different users. They predict a user's QoS on an unknown service by exploiting the historical QoS experience of similar users. Nevertheless, when only limited historical QoS data is available, these approaches either fail to make any predictions or make very poor ones. The cornerstone of the proposed QoS evaluation scheme is a Relational Clustering based Model (or RCM) that effectively addresses the data scarcity issue as stated above. Experimental results on both real and synthetic datasets demonstrate that the proposed scheme can more accurately predict the QoS on unknown service providers. The efficient performance also makes it applicable to QoS evaluation for large scale Web services.