Abstract
Collaborative filtering algorithms help people make choices based on the opinions of other people. User-based and item-based collaborative filtering algorithms predict new ratings by using ratings of similar users or items. Similarity calculation is the key step in the algorithms. This paper compares the prediction quality of four commonly used similarity measures on different datasets. Experimental results show that Adjusted Cosine similarity consistently achieves best prediction accuracy.
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References
Xu, H.L., Wu, X., Li, X.D., Yan, B.P.: Comparison study of internet recommendation system. J. Softw. 20(2), 350–362 (2009)
Ma, H.W., Zhang, G.W., Li, P.: Survey of collaborative filtering algorithm. J. Chin. Comput. Syst. 30(7), 1282–1288 (2009)
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave information tapestry. Commun. ACM 35(12), 61–70 (1992)
Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: ACM CSCW, pp. 175–186 (1994)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th Proceeding of WWW Conference, Hong Kong, 1–5 May 2001
Wang, J., Yin, J.: Enhancing accuracy of user-based collaborative filtering recommendation algorithm in social network. In: International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM 2012), pp. 142–145. IEEE Press (2012)
Zhou, J.F., Tang, X., Guo, J.F.: An optimized collaborative filtering recommendation algorithm. J. Comput. Res. Dev. 14(10), 1842–1847 (2004)
Tata, S., Patel, J.M.: Estimating the selectivity of tf-idf based cosine similarity predicates. ACM Sigmod Rec. 36(2), 7–12 (2007)
Li, B.: Distance weighted cosine similarity measure for text classification. In: Ideal 2013, vol. 8206, pp. 611–618 (2013)
Guo, L., Peng, Q.K.: A combinative similarity computing measure for collaborative filtering. Appl. Mech. Mater. 347–350, 2919–2925 (2013)
Shabanpoor, M., Soofipoor, S., Mahdavi, M.: An E-health recommender system based on adjusted cosine similarity. Int. J. Acad. Res. 7(5), 84–90 (2015)
Deng, S., Cao, H., Pan, Q., Shen, J., Xing, T.: Analysis and improvement of affinity propagation based on Euclidean distance similarity. J. Comput. Inf. Syst. 8(6), 2293–2300 (2012)
Kumar, A., Panigrahi, R.K.: Classification of hybrid-pol data based on Euclidean distance between Stokes vectors. In: IEEE Radar Conference, pp. 422–425 (2015)
Acknowledgements
This work was financially supported by 2016 Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing at the Sun Yat-sen University, 2014 Youth Innovative Talents Project (Natural Science) of Education Department of Guangdong Province (2014KQNCX248), 2016 Characteristic Innovation Project (Natural Science) of Education Department of Guangdong Province of China (2016KTSCX162), and Foshan Science and Technology Bureau Project (2016AG100382).
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Wang, J. (2018). Comparison of Similarity Measures in Collaborative Filtering Algorithm. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2017. Lecture Notes in Electrical Engineering, vol 464. Springer, Singapore. https://doi.org/10.1007/978-981-10-7398-4_37
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DOI: https://doi.org/10.1007/978-981-10-7398-4_37
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