An Experimental Study on the Accuracy and Efficiency of Some Similarity Measures for Collaborative Filtering Recommender Systems

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Published: Feb 28, 2021
Keywords:
Similarity Measures, Recommender Systems, Collaborative Filtering, Accuracy, Efficiency

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Abba Almu
Ziya’u Bello

Abstract

Similarity measures are the core component used by the neighborhood based collaborative filtering recommendations to predict the user's ratings in item-based or user-based recommender algorithms. The collaborative filtering has been implemented with different similarity measures but ignores to consider the time taken by the similarity measures to make accurate predictions in different application domains. This paper intended to help recommender systems developers to identify suitable similarity measure depending upon the application domain to be used with less execution time and error rate. It also takes the effect of neighbrhood sizes (k) on the prediction accuracy and efficiency into consideration. The experimental evaluations were conducted on the four similarity measures with the same dataset using Python programming language implementation. The evaluation metrics considered during the experiments are Execution Time, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the evaluation demonstrated that, Manhattan Distance similarity measure had the best accuracy as well as the efficiency of predictions in this study.

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How to Cite
[1]
Abba Almu and Ziya’u Bello, “An Experimental Study on the Accuracy and Efficiency of Some Similarity Measures for Collaborative Filtering Recommender Systems”, Int. J. Comput. Eng. Res. Trends, vol. 8, no. 2, pp. 33–39, Feb. 2021.
Issue
Vol. 8 No. 2 (2021): February (2021) Issue
Section
Research Articles
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References

Ajay A. & Minakshi C. Similarity Measures used in Recommender Systems: A Study: International Journal of Engineering Technology Science and Research (IJETSR), 2017, 4 (6): 619-626.

Ali S. S., Saeed A., & Teh Y. W. A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data: PLoS ONE, 2015, 10 (12): 1-20.

Elmutafa, S. A. A. Principle of Computer System: LAMBERT Academic Publishing (LAP), 2015, pp. 1-2.

Jayvardhan , S., Thomas, V., and Yadav, M. L. Prediction Accuracy Comparison Of Similarity Measures In Memory Based Collaborative Filtering Recommender Systems. International Journal of Engineering Research & Technology (IJERT), 2013, 2 (6): 1105- 1108.

Lamis, A. H., Chadi A. J., Jacques B. A., & Jacques D. Similarity Measures for Collaborative Filtering Recommender Systems IEEE Middle East and North Africa Communications Conference (MENACOMM), 2018, pp. 1-5.

Madhuri A. B. Comparative study of similarity measures for item based top n recommendation. Unpublished project for Bachelor of Technology Degree in Computer Science and Engineering: National Institute of Technology, Rourkela (Deemed University), 2014.

Harper, F. M., & Konstan, J. A. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS), 2015, 5(4): 1–19.

Minh-Phung T. D., Dung V. N, & Loc N. Model-based Approach for Collaborative Filtering: The 6th International Conference on Information Technology for Education (IT@EDU2010) Ho Chi Minh city,2010, Vietnam. 218-219.

Odunayo, E. O., Ibrahim A.A., Adeleye, S. F., & Olumide, O. O. A Comparative Analysis of Euclidean Distance and Cosine Similarity Measure for Automated Essay-Type Grading: Journal of Engineering and Applied Sciences, 2018, 13 (11): 4198¬-4204.

Perlibakas, V. Face Recognition Using Principal Component Analysis and Wavelet Packe Decomposition. Informatica, Lith. Acad. Sci., 2004, 15 (2) pp. 243-250.

Ping, H. Q., and Ming X. Research on Several Recommendation Algorithms. Procedia Engineering, 2012, 29: 2427-2431.

} Shalini C. S., Hong X.., & Shri R. Measures of Similarity in Memory-Based Collaborative Filtering Recommender System – A Comparison: MISNC '17 Bangkok, 2017, Thailand, pp.1-8.

Suganeshwari, G., & Syed I. S. P. A Comparison Study on Similarity Measures in Collaborative Filtering Algorithms for Movie Recommendation: International Journal of Pure and Applied Mathematics, 2018, 119 (15):1495-1505.

Taner A. Efecan K. & Zeki B. Comparison of Collaborative Filtering Algorithms with various Similarity Measures for Movie Recommendation: International Journal of Computer Science, Engineering and Applications (IJCSEA), 2016, 6 (3):12-13.