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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Bibliographic Details
Published inArtificial Intelligence in Theory and Practice III pp. 165 - 173
Main Authors Suchal, Ján, Návrat, Pavol
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2010
SeriesIFIP Advances in Information and Communication Technology
Subjects
full text search
recommendation systems
Online AccessGet full text
ISBN9783642152856
3642152856
ISSN1868-4238
1868-422X
1868-422X
DOI10.1007/978-3-642-15286-3_16

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Summary: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.
ISBN:9783642152856
3642152856
ISSN:1868-4238
1868-422X
1868-422X
DOI:10.1007/978-3-642-15286-3_16