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Performance Evaluation of Recommender Systems

Volume 13, Number 8, December 2017, pp. 1246-1256
DOI: 10.23940/ijpe.17.08.p7.12461256

Mingang Chena,b, Pan Liuc

aShanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China
bShanghai Development Center of Computer Software Technology, Shanghai 201112, China
cShanghai Business School, Shanghai 201112, China

(Submitted on October 29, 2017; Revised on November 17, 2017; Accepted on December 1, 2017)



Abstract:

Recommender systems play an important role in e-commerce. This paper discusses three classical methods - offline analytics, user study, and online experiment - to evaluate the performance of recommender systems and also analyzes their application scenarios. Some performance evaluation metrics of recommender systems are reviewed and summarized from four perspectives (machine learning, information retrieval, human-computer interaction and software engineering) combined with the above three evaluation methods. These evaluation methods and evaluation metrics summarized in the paper provide the designers with guidance for the comprehensive evaluation and selection of recommended algorithms.

 

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