Ibrahim Yakut, Huseyin Polat, Mehmet Koc


Collaborative filtering (CF) systems are effective solutions for information overload problem while contributing web personalization. Different memory-based algorithms operating over entire data set have been utilized for CF purposes. However, they suffer from scalability, sparsity, and cold start problems. In this study, in order to overcome such problems, we propose a new approach based on projection matrix resulted from principal component analysis (PCA). We analyze the proposed scheme computationally; and show that it guarantees scalability while getting rid of sparsity and cold start problems. To evaluate the overall performance of the scheme, we perform experiments using two well-known real data sets. The results demonstrate that our scheme is able to provide accurate predictions efficiently. After analyzing the outcomes, we present some suggestions.


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Paper Citation

in Harvard Style

Yakut I., Polat H. and Koc M. (2010). PCF: PROJECTION-BASED COLLABORATIVE FILTERING . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 408-413. DOI: 10.5220/0003071204080413

in Bibtex Style

author={Ibrahim Yakut and Huseyin Polat and Mehmet Koc},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
SN - 978-989-8425-28-7
AU - Yakut I.
AU - Polat H.
AU - Koc M.
PY - 2010
SP - 408
EP - 413
DO - 10.5220/0003071204080413