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Authors: Shlomo Berkovsky ; Yaniv Eytani and Larry Manevitz

Affiliation: University of Haifa, Israel

Keyword(s): Collaborative Filtering, Recommender Systems, Content-Addressable Systems.

Related Ontology Subjects/Areas/Topics: Applications of Expert Systems ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Data Engineering ; Enterprise Information Systems ; Group Decision Support Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Ontologies and the Semantic Web ; Society, e-Business and e-Government ; Web Information Systems and Technologies

Abstract: Collaborative Filtering (CF) is one of the most popular recommendation techniques. It is based on the assumption that users with similar tastes prefer similar items. One of the major drawbacks of the CF is its limited scalability, as the complexity of the CF grows linearly both with the number of available users and items. This work proposes a new fast variant of the CF employed over multi-dimensional content-addressable space. Our approach heuristically decreases the computational effort required by the CF algorithm by limiting the search process only to potentially similar users. Experimental results demonstrate that our approach is capable of generate recommendations with high levels of accuracy, while significantly improving performance in comparison with the traditional implementation of the CF.

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Paper citation in several formats:
Berkovsky, S.; Eytani, Y. and Manevitz, L. (2006). COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING. In Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-972-8865-42-9; ISSN 2184-4992, SciTePress, pages 91-98. DOI: 10.5220/0002454100910098

@conference{iceis06,
author={Shlomo Berkovsky. and Yaniv Eytani. and Larry Manevitz.},
title={COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING},
booktitle={Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2006},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002454100910098},
isbn={978-972-8865-42-9},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING
SN - 978-972-8865-42-9
IS - 2184-4992
AU - Berkovsky, S.
AU - Eytani, Y.
AU - Manevitz, L.
PY - 2006
SP - 91
EP - 98
DO - 10.5220/0002454100910098
PB - SciTePress

- Science and Technology Publications, Lda.
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