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Enhanced vector space models for content-based recommender systems

Enhanced vector space models for content-based recommender systems Enhanced Vector Space Models for Content-based Recommender Systems Cataldo Musto Dept. of Computer Science University of Bari, Italy cataldomusto@di.uniba.it ABSTRACT The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice within the scienti c community. The reason is twofold: rst, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplicity does not hurt the e €ectiveness of the model. Although Information Retrieval and Information Filtering undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analzyed. The goal of this work is to investigate the impact of vector space models in the Information Filtering area. Speci cally, I will introduce two approaches: the rst one, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimensionality and the inability to manage the semantics of documents. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an experimental evaluation performed on http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Enhanced vector space models for content-based recommender systems

Association for Computing Machinery — Sep 26, 2010

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References (199)

Datasource
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISBN
978-1-60558-906-0
doi
10.1145/1864708.1864791
Publisher site
See Article on Publisher Site

Abstract

Enhanced Vector Space Models for Content-based Recommender Systems Cataldo Musto Dept. of Computer Science University of Bari, Italy cataldomusto@di.uniba.it ABSTRACT The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice within the scienti c community. The reason is twofold: rst, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplicity does not hurt the e €ectiveness of the model. Although Information Retrieval and Information Filtering undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analzyed. The goal of this work is to investigate the impact of vector space models in the Information Filtering area. Speci cally, I will introduce two approaches: the rst one, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimensionality and the inability to manage the semantics of documents. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an experimental evaluation performed on

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