Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System
In this paper we present a method that allows us to use a generic full text engine as a k-nearest neighbor-based recommendation system. Experiments on two real world datasets show that accuracy of recommendations yielded by such system are comparable to existing spreading activation recommendation t...
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| Published in | Artificial Intelligence in Theory and Practice III pp. 165 - 173 |
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| Main Authors | , |
| Format | Book Chapter |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2010
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| Series | IFIP Advances in Information and Communication Technology |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783642152856 3642152856 |
| ISSN | 1868-4238 1868-422X 1868-422X |
| DOI | 10.1007/978-3-642-15286-3_16 |
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| Abstract | In this paper we present a method that allows us to use a generic full text engine as a k-nearest neighbor-based recommendation system. Experiments on two real world datasets show that accuracy of recommendations yielded by such system are comparable to existing spreading activation recommendation techniques. Furthermore, our approach maintains linear scalability relative to dataset size. We also analyze scalability and quality properties of our proposed method for different parameters on two open-source full text engines (MySQL and SphinxSearch) used as recommendation engine back ends. |
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| AbstractList | In this paper we present a method that allows us to use a generic full text engine as a k-nearest neighbor-based recommendation system. Experiments on two real world datasets show that accuracy of recommendations yielded by such system are comparable to existing spreading activation recommendation techniques. Furthermore, our approach maintains linear scalability relative to dataset size. We also analyze scalability and quality properties of our proposed method for different parameters on two open-source full text engines (MySQL and SphinxSearch) used as recommendation engine back ends. |
| Author | Návrat, Pavol Suchal, Ján |
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| Editor | Bramer, Max |
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| PublicationSubtitle | Third IFIP TC 12 International Conference on Artificial Intelligence, IFIP AI 2010, Held as Part of WCC 2010, Brisbane, Australia, September 20-23, 2010. Proceedings |
| PublicationTitle | Artificial Intelligence in Theory and Practice III |
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| Snippet | In this paper we present a method that allows us to use a generic full text engine as a k-nearest neighbor-based recommendation system. Experiments on two real... |
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| SubjectTerms | full text search recommendation systems |
| Title | Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System |
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