Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
A system for principled matchmaking in an electronic marketplace
WWW '03 Proceedings of the 12th international conference on World Wide Web
A software framework for matchmaking based on semantic web technology
WWW '03 Proceedings of the 12th international conference on World Wide Web
Focusing on Context in Human-Centered Computing
IEEE Intelligent Systems
Service-based software: the future for flexible software
APSEC '00 Proceedings of the Seventh Asia-Pacific Software Engineering Conference
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Adapting Content for Wireless Web Services
IEEE Internet Computing
Context for Personalized Web Services
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Dynamic Selection of Web Services with Recommendation System
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
Integrating the physical world with the web to enable context-enhanced mobile services
Mobile Networks and Applications
What can context do for web services?
Communications of the ACM - Software product line
Context-based matching for Web service composition
Distributed and Parallel Databases
Granular best match algorithm for context-aware computing systems
Journal of Systems and Software
WSRec: A Collaborative Filtering Based Web Service Recommender System
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Making the difference in semantic web service composition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Context-Based Matching and Ranking of Web Services for Composition
IEEE Transactions on Services Computing
Non-standard inferences in description logics
Non-standard inferences in description logics
Computing least common subsumers in description logics
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Using Context Similarity for Service Recommendation
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Information seeking: convergence of search, recommendations, and advertising
Communications of the ACM
A Hybrid Approach to Recommending Semantic Software Services
ICWS '11 Proceedings of the 2011 IEEE International Conference on Web Services
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
iSeM: approximated reasoning for adaptive hybrid selection of semantic services
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
Adaptive Hybrid Semantic Selection of SAWSDL Services with SAWSDL-MX2
International Journal on Semantic Web & Information Systems
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The current proliferation of software services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional services, yet their direct application to software services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune software services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.