Machine Learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Data mining for customer service support
Information and Management
Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
WWW '05 Proceedings of the 14th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Web Customer Modeling for Automated Session Prioritization on High Traffic Sites
UM '07 Proceedings of the 11th international conference on User Modeling
OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services
Web Semantics: Science, Services and Agents on the World Wide Web
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There are various approaches to solve the problem of services selection according to user request. Usually, ontology-based filtering is used. The problem occurs if the service can be run a limited number of times and the number of users interested in service execution is higher than the number of possible service executions. In this work we propose the knowledge-based algorithm for limited services selection. It is assumed that knowledge of users is used to determine priorities that are used in a process of services selection. The users' priorities are obtained using clustering and classification methods. The results of testing the accuracy of classifiers for users' priorities determining and the quality of the algorithm for limited services selection are presented.