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

Abba Almu, Ziya’u Bello, , ,
Dept. of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, P.M.B 2346, Sokoto – Nigeria.

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.

Abba Almu,Ziya’u Bello."An Experimental Study on the Accuracy and Efficiency of Some Similarity Measures for Collaborative Filtering Recommender Systems ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.8, Issue 02,pp.33-39, February - 2021, URL :,

Keywords : Similarity Measures, Recommender Systems, Collaborative Filtering, Accuracy, Efficiency

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