Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System

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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Communication dans un congrès
Max Bramer. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-331, pp.165-173, 2010, Artificial Intelligence in Theory and Practice III. 〈10.1007/978-3-642-15286-3_16〉
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Ján Suchal, Pavol Návrat. Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System. Max Bramer. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. Springer, IFIP Advances in Information and Communication Technology, AICT-331, pp.165-173, 2010, Artificial Intelligence in Theory and Practice III. 〈10.1007/978-3-642-15286-3_16〉. 〈hal-01054596〉

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