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Seven Factors in Evaluating Recommender System
Kang, Liting; Wang, Yong
First AuthorKang, Liting
Source PublicationApplied Mechanics and Materials
Correspondent ;
Contribution Rank1

Recommender system (RS) has been evaluated in many but incomparable ways beyond accuracy and thus proposing an evaluation framework to synthesize the existing strategies seems a solution. However, few scholars did it so far. Through literature review, user interview and expert assessment, this study proposed a theoretical evaluation model of RS and then formed the assessment tool, RS Evaluation Questionnaire (RSE). The results showed that RSE was an effective tool to evaluate a recommender system, with its reliability (Cronbach’s α=0.803) and validity meeting the requirements of psychometrics. Seven factors such as Perceived Quality and Perceived Ease of Use were generated by factor analysis, accounting for 63.126% of the variance. Furthermore, regression analysis indicated that different combinations of RSE factors could significantly predict User Satisfaction, Reuse Intention and positive Word-Of-Mouth (WOM) spreading willingness. Enlightenments for future research and practice were discussed as well in the end.

Keywordpersonalized recommender system ser-centered evaluation psychometric questionnaire user interview
Indexed ByEI
Citation statistics
Document Type期刊论文
Corresponding AuthorKang, Liting; Wang, Yong
AffiliationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kang, Liting,Wang, Yong. Seven Factors in Evaluating Recommender System[J]. Applied Mechanics and Materials,2014,472(不详):443-449.
APA Kang, Liting,&Wang, Yong.(2014).Seven Factors in Evaluating Recommender System.Applied Mechanics and Materials,472(不详),443-449.
MLA Kang, Liting,et al."Seven Factors in Evaluating Recommender System".Applied Mechanics and Materials 472.不详(2014):443-449.
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File name: Kang, L.T., Wang Y. (2014). Seven Factors in Evaluating Recommender System. AMM.472:443-449.pdf
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