Selecting appropriate metrics for evaluation of recommender systems
Автор: Bhupesh Rawat, Sanjay K.Dwivedi
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 1 Vol. 11, 2019 года.
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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.
Precision, Recall, Root Mean Square Error, Mean Absolute Error, Normalized Mean Average Error
Короткий адрес: https://sciup.org/15016327
IDR: 15016327 | DOI: 10.5815/ijitcs.2019.01.02
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