# Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems

### Mohammad Yahya H. Al-Shamri, Nagi H. Al-Ashwal

#### Abstract

Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. The most popular similarity measure for memory-based CRS is the Pearson correlation coefficient which measures how much the two users are correlated. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This will be reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to Pearson correlation coefficient. In this paper we propose a fuzzy weighting to the Pearson correlation coefficient which takes into account the different rating scales of different users so that the rating deviation from the user’s mean rating is fuzzified not the rating itself. The experimental results show that Pearson correlation coefficient with fuzzy weighting outperforms the traditional approaches.

#### References

- Goldberg, D., Nichols, D., Oki, B.M., and Terry, D., 1992. 'Using collaborative filtering to weave an information Tapestry'. Communication of the ACM, vol. 35 (12), pp. 61-70.
- Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S., 2007. 'Collaborative filtering recommender systems. In the Adaptive Web', LNCS 4321, P. Brusilovsky, A. Kobsa, and W. Nejdl. Eds., Berlin Heidelberg: Springer-Verlag, pp. 291 - 324.
- Burke, R., 2002. 'Hybrid recommender systems: survey and experiments'. User Modeling and User-Adapted Interaction, vol. 12, pp. 331-370.
- Adomavicius, G., and Tuzhilin, A., 2005. 'Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions'. IEEE Trans. on Knowledge and Data Eng., vol. 17(6), pp. 734-749.
- Al-Shamrri, Mohammad Yahya H., and Bharadwaj, Kamal K., 2008. 'Fuzzy-genetic approach to recommender systems based on a novel hybrid user model'. Expert Systems with Applications, Elsevier, vol. 35(3), pp. 1386-1399.
- Bobadilla, J., Ortega, F., Hernando, A., and Alcala, J., 2011. 'Improving collaborative filtering recommender system results and performance using genetic algorithms'. Knowledge Based Systems, Elsevier, vol. 24(8), pp. 1310-1316.
- Min S-H., and Han, I., 2005. 'Optimizing collaborative filtering recommender systems'. In AWIC 2005, LNAI 3528, P.S. Szczepaniak and A. Niewiadomski. Eds., Berlin Heidelberg, Springer-Verlag, pp. 313-319.
- Bharadwaj, Kamal K., and Al-Shamri, Mohammad Yahya H., 2009. 'Fuzzy computational models for trust and reputation systems'. Electronic Commerce Research and Applications, Elsevier, vol. 8, pp. 37-47.
- Breese, J., Heckerman, D., and Kadie, C., 1998. 'Empirical analysis of predictive algorithms for collaborative filtering'. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43-52, Madison, WI. Morgan Kaufmann, San Francisco, CA.
- Vozalis, E., and Margaritis, K., 2003. 'Analysis of recommender systems' algorithms'. In Proceedings of the sixth Hellenic-European Conference on Computer Mathematics and its Applications (HERCMA), Athens, Greece.
- Han, J., and Kamber, M., 2006. Data Mining, Concepts and Techniques. Morgan Kaufmann Publishers, 2nd edition.
- Herlocker, J., Konstan, L., Terveen, L., and Riedl, J., 2004. 'Evaluating collaborative filtering recommender systems'. ACM Transaction on Informartion Systems, vol. 22(1), pp. 5-53.

#### Paper Citation

#### in Harvard Style

Yahya H. Al-Shamri M. and H. Al-Ashwal N. (2013). **Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems** . In *Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,* ISBN 978-989-8565-59-4, pages 409-414. DOI: 10.5220/0004412404090414

#### in Bibtex Style

@conference{iceis13,

author={Mohammad Yahya H. Al-Shamri and Nagi H. Al-Ashwal},

title={Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems},

booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

year={2013},

pages={409-414},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0004412404090414},

isbn={978-989-8565-59-4},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,

TI - Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems

SN - 978-989-8565-59-4

AU - Yahya H. Al-Shamri M.

AU - H. Al-Ashwal N.

PY - 2013

SP - 409

EP - 414

DO - 10.5220/0004412404090414