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Evaluating the Quality of Recommendation System by Using Serendipity Measure
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논문 기본 정보

Type
Academic journal
Author
Tserendulam Dorjmaa (Yonsei University MIRAE Campus) Taeksoo Shin (Yonsei University)
Journal
Korea Intelligent Information Systems Society Journal of Intelligence and Information Systems Vol.25 No.4 KCI Accredited Journals
Published
2019.12
Pages
89 - 103 (15page)

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Evaluating the Quality of Recommendation System by Using Serendipity Measure
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Abstract· Keywords

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Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user’s decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.
#Recommendation system #Serendipity measure #Unexpectedness #Relevance #Quality #추천시스템 #세렌디피티 지표 #비기대성 #관련성 #품질

Contents

1. Introduction
2. Several issues in Recommender System
3. Research methodology
4. Experimental Results
5. Conclusion
References
국문요약

References (25)

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Adamopoulos, P. and Tuzhilin, A., “On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected,” ACM Transactions on Intelligent Systems and Technology, Vol.1, No.1(2014), 1~51. google schola Adomavicius, G. and Tuzhilin, A., “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6(2005), 734~749. google schola Aggarwal, C. C., Procopiuc, C., and Yu, P. S., “Finding Localized Associations in Market Basket Data,” IEEE Transactions on Knowledge and Data Engineering, Vol.14, No.1(2002), 51~62. google schola Bernardi, L., Kamps, J., Kiseleva, J., and Muller, M., “The Continuous Cold Start Problem in e-Commerce Recommender Systems,” 2nd Workshop on New Trends on Content-Based Recommender Systems, (2015), 30~33. google schola Chen, Y., Wu, C., Xie, M. and Guo, X., “Solving the Sparsity Problem in Recommender Systems Using Association Retrieval,” Journal of Computers, Vol.6, No.9(2011), 1896~1902. google schola

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