COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING

Shlomo Berkovsky, Yaniv Eytani, Larry Manevitz

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.

References

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Paper Citation


in Harvard Style

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 2: ICEIS, ISBN 978-972-8865-42-9, pages 91-98. DOI: 10.5220/0002454100910098


in Bibtex Style

@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 2: ICEIS,},
year={2006},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002454100910098},
isbn={978-972-8865-42-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - COLLABORATIVE FILTERING BASED ON CONTENT ADDRESSING
SN - 978-972-8865-42-9
AU - Berkovsky S.
AU - Eytani Y.
AU - Manevitz L.
PY - 2006
SP - 91
EP - 98
DO - 10.5220/0002454100910098