Selecting Appropriate Metrics for Evaluation of Recommender Systems

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Bhupesh Rawat 1,* Sanjay k. Dwivedi 1

1. Department of Computer Science, BBAU, Lucknow, 226025, India

* Corresponding author.


Received: 7 Jun. 2018 / Revised: 6 Aug. 2018 / Accepted: 22 Sep. 2018 / Published: 8 Jan. 2019

Index Terms

Precision, Recall, Root Mean Square Error, Mean Absolute Error, Normalized Mean Average Error


The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and e-government among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.

Cite This Paper

Bhupesh Rawat, Sanjay K.Dwivedi, "Selecting Appropriate Metrics for Evaluation of Recommender Systems", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.1, pp.14-23, 2019. DOI:10.5815/ijitcs.2019.01.02


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