oalogo2  

AUTHOR(S):

Samiyah Al-Anazi, Pandian Vasant, M. Abdullah-Al-Wadud

 

TITLE

An Improved Similarity Metric for Recommender Systems

pdf PDF

ABSTRACT

Due to pervasive technologies in various applications, which are used in our everyday lives, recommender systems have become widely used in most of these applications to estimate the users’ needs depending on his/her preferences. The development of recommendation methods typically focuses on maximizing the prediction accuracy of the users’ interests. Currently, collaborative filtering (CF) is a widely used approach for recommender systems. The similarity measures play a major role in such recommender systems. In spite of the availability of many different similarity measures, user similarity is yet to be calculated perfectly in recommender systems. We propose a similarity metric that helps to increase the accuracy of recommended items.

KEYWORDS

Recommender system, Collaborative filtering, Content-based filtering, Similarity, Pearson correlation

REFERENCES 

[1] F. Ricci, L. Rokach and B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook, 1st ed., F. Ricci, L. Rokach, B. Shapira and P. Kantor, Ed. New York: Springer-Verlag New York, 2010, pp. 1-35.

[2] S. Jain, A. Grover, P. Thakur and S. Choudhary, Trends, Problems And Solutions of Recommender System, in International Conference on Computing, Communication and Automation (ICCCA2015), 2015.

[3] Bellogin A, de Vries AP, Understanding similarity metrics in neighbour-based recommender systems, In Proceedings of the 2013 conference on the theory of information retrieval (ICTIR ’13), ACM, New York, USA, 2013, pp 48–55

[4] G. Guo, J. Zhang, and N. Yorke-Smith, A novel bayesian similarity measure for recommender systems, in Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.

[5] M. Robillard and R. Walker, An Introduction to Recommendation Systems in Software Engineering', in Recommendation Systems in Software Engineering, 1st ed., M. Robillard, W. Maalej, R. Walker and T. Zimmermann, Ed. Software, IEEE (Volume:27 , Issue: 4 ): IEEE, 2010, pp. pp: 1-11.

[6] R. Prasad, A Categorical Review of Recommender Systems, International Journal of Distributed and Parallel systems, vol. 3, no. 5, 2012, pp. 73-83.

[7] J. Schafer, D. Frankowski, J. Herlocker and S. Sen, Collaborative Filtering Recommender Systems, in The Adaptive Web: Methods and Strategies of Web Personalization, 1st ed., P. Brusilovsky, A. Kobsa and W. Nejdl, Ed. Springer Berlin Heidelberg, 2007, pp. pp: 291- 324.

[8] G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, 2005, pp. 734-749.

[9] L. Zhongduo, Indoor Location-Based Recommender System, Master's thesis, University of Toronto, Department of Electrical and Computer Engineering, August 2013.

[10] S. Spiegel, A Hybrid Approach to Recommender Systems based on Matrix Factorization, Technical University Berlin, Department for Agent Technologies and Telecommunications, 2009.

[11] S. Bouhali, A. H and M. Aïcha, Handling preferences under uncertainty in recommender systems, in IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014, 2014, pp. 2262 - 2269.

[12] J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon and J. Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, vol. 40, no. 3,1997, pp. 77-87.

[13] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Item-based collaborative filtering recommendation algorithms, in Proceedings of the International Conference on the World Wide Web, 2001, pp. 285–295.

[14] S. Owen, R. Anil, T. Dunning, and E. Friedman. Mahout in action, 2010.

Cite this paper

Samiyah Al-Anazi, Pandian Vasant, M. Abdullah-Al-Wadud. (2016) An Improved Similarity Metric for Recommender Systems. International Journal of Computers, 1, 154-157

 

cc.png
Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0