Journal of Applied Information Science

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Performance Comparison of Similarity Measures Used in Recommendation Systems


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1 Ankara University, Computer Engineering Department, Ankara, Turkey
     

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There are more data on the web, thus it is hard to get relevant data and make good decisions. Recommendation systems provide suggestions to users about the various items. They are classified into four groups which are collaborative filtering, content-based filtering, knowledge-based recommender systems, and hybrid recommendation systems. There are some similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood to calculate similarity between users or items. In this study, a user-based collaborative filtering recommendation system is developed on Eclipse platform using mahout library. To develop a recommendation system, different similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood are used. After that, recommendation performances of them are compared. Movielens datasets are used to train and test the system. As a result, it is seen that while the best mean average error and the best ischolar_main mean square error performances belong to Uncentred Cosine similarity measure, the best precision, recall, and f-measure performances belong to Pearson Correlation measurement.

Keywords

Euclidean, Loglikelihood, Pearson, Recommendation, Similarity.
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  • Performance Comparison of Similarity Measures Used in Recommendation Systems

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Authors

Berna Seref
Ankara University, Computer Engineering Department, Ankara, Turkey
Erkan Bostanci
Ankara University, Computer Engineering Department, Ankara, Turkey

Abstract


There are more data on the web, thus it is hard to get relevant data and make good decisions. Recommendation systems provide suggestions to users about the various items. They are classified into four groups which are collaborative filtering, content-based filtering, knowledge-based recommender systems, and hybrid recommendation systems. There are some similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood to calculate similarity between users or items. In this study, a user-based collaborative filtering recommendation system is developed on Eclipse platform using mahout library. To develop a recommendation system, different similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood are used. After that, recommendation performances of them are compared. Movielens datasets are used to train and test the system. As a result, it is seen that while the best mean average error and the best ischolar_main mean square error performances belong to Uncentred Cosine similarity measure, the best precision, recall, and f-measure performances belong to Pearson Correlation measurement.

Keywords


Euclidean, Loglikelihood, Pearson, Recommendation, Similarity.

References