GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
A vector space model for automatic indexing
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Communications of the ACM - E-services: a cornucopia of digital offerings ushers in the next Net-based evolution
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A Vector Space Search Engine forWeb Services
ECOWS '05 Proceedings of the Third European Conference on Web Services
Dynamic Selection of Web Services with Recommendation System
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Improving Web Service Discovery with Usage Data
IEEE Software
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Study of an Algorithm of Web Service Matching Based on Semantic Web Service
ALPIT '07 Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)
Introduction to Information Retrieval
Introduction to Information Retrieval
Web Services Discovery Based on Latent Semantic Approach
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
Latent Semantic Analysis --- The Dynamics of Semantics Web Services Discovery
Advances in Web Semantics I
WSRec: A Collaborative Filtering Based Web Service Recommender System
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
A New Web Services Matching Algorithm
IUCE '09 Proceedings of the 2009 International Symposium on Intelligent Ubiquitous Computing and Education
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Web service discovery based on past user experience
BIS'07 Proceedings of the 10th international conference on Business information systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UsageQoS: Estimating the QoS of Web Services through Online User Communities
ACM Transactions on the Web (TWEB)
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The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages, and notations, which easily decrease recommendation's efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.