Improving sparsity and new user problems in collaborative filtering by clustering the personality factors-Reference-Cited by-同舟云学术

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Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

Published:2018-01-12 Issue:4 Volume:18 Page:813-836
ISSN:1389-5753
Container-title:Electronic Commerce Research
language:en
Short-container-title:Electron Commer Res

Author:

Yusefi Hafshejani Zahra,Kaedi Marjan,Fatemi Afsaneh

Publisher

Springer Science and Business Media LLC

Subject

Human-Computer Interaction,Economics, Econometrics and Finance (miscellaneous)

Link

http://link.springer.com/article/10.1007/s10660-018-9287-x/fulltext.html

Reference81 articles.

1. Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-based active learning for collaborative filtering recommender systems. In AI* IA 2013: Advances in Artificial Intelligence, pp. 360–371, Springer.

2. Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive web, Volume 4321 of the series Lecture Notes in Computer Science (pp. 377–408). Springer, Berlin/Heidelberg.

3. Bilge, A., & Polat, H. (2013). A scalable privacy-preserving recommendation scheme via bisecting k-means clustering. Information Processing and Management, 49, 912–927.

4. Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39, 10059–10072.

5. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

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