EFFICIENT NEIGHBOURHOOD ESTIMATION FOR RECOMMENDATION MAKING

Li-Tung Weng, Yue Xu, Yuefeng Li, Richi Nayak

Abstract

Recommender systems produce personalized product recommendations during a live customer interaction, and they have achieved widespread success in e-commerce nowadays. For many recommender systems, especially the collaborative filtering based ones, neighbourhood formation is an essential algorithm component. Because in order for collaborative-filtering based recommender to make a recommendation, it is required to form a set of users sharing similar interests to the target user. “Best-k-neighbours” is a popular neighbourhood formation technique commonly used by recommender systems, however as tremendous growth of customers and products in recent years, the computation efficiency become one of the key challenges for recommender systems. Forming neighbourhood by going through all neighbours in the dataset is not desirable for large datasets containing million items and users. In this paper, we presented a novel neighbourhood estimation method which is both memory and computation efficient. Moreover, the proposed technique also leverages the common “fixed-n-neighbours” problem for standard “best-k- neighbours” techniques, therefore allows better recommendation quality for recommenders. We combined the proposed technique with a taxonomy-driven product recommender, and in our experiment, both time efficiency and recommendation quality of the recommender are improved.

References

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Paper Citation


in Harvard Style

Weng L., Xu Y., Li Y. and Nayak R. (2008). EFFICIENT NEIGHBOURHOOD ESTIMATION FOR RECOMMENDATION MAKING . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS, ISBN 978-989-8111-39-5, pages 12-19. DOI: 10.5220/0001695000120019


in Bibtex Style

@conference{iceis08,
author={Li-Tung Weng and Yue Xu and Yuefeng Li and Richi Nayak},
title={EFFICIENT NEIGHBOURHOOD ESTIMATION FOR RECOMMENDATION MAKING},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS,},
year={2008},
pages={12-19},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001695000120019},
isbn={978-989-8111-39-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS,
TI - EFFICIENT NEIGHBOURHOOD ESTIMATION FOR RECOMMENDATION MAKING
SN - 978-989-8111-39-5
AU - Weng L.
AU - Xu Y.
AU - Li Y.
AU - Nayak R.
PY - 2008
SP - 12
EP - 19
DO - 10.5220/0001695000120019