User Curiosity Factor in Determining Serendipity of Recommender System

Arseto Satriyo Nugroho(1*), Igi Ardiyanto(2), Teguh Bharata Adji(3)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author


Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendipity. Serendipity can be described as an unexpected discovery that is useful. Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity is curiosity. While some researchers already addressed curiosity factor, it is found that the relationships between various serendipity component as perceived by the users and their curiosity levels is still yet to be researched. In this paper, the method to determine user curiosity model by considering the variation of rated items was presented, then relation to serendipity components using existing user feedback data was validated. The finding showed that the curiosity model was related to some user-perceived values of serendipity, but not all. Moreover, it also had positive effect on broadening the user preference.



Recommender System;Serendipity;Relevance;Novelty;Unexpectedness;Evaluation Metrics;Curiosity

Full Text:



F. Ricci, B. Shapira, and L. Rokach, Eds., Recommender Systems Handbook, 2nd ed., New York, USA: Springer US, 2015.

C. Anderson, “The Long Tail: Why the Future of Business Is Selling Less of More,” New York, USA: Hachette Books, 2006.

H. Yin, B. Cui, J. Li, J. Yao, and C. Chen, “Challenging the Long Tail Recommendation,” Proceedings of the VLDB Endowment (PVLDB), 2012, pp. 896-907.

R. Burke, “Hybrid Web Recommender Systems,” in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds., Berlin, Germany: Springer-Verlag, 2007, pp. 377-408.

D. Kotkov, J. Veijalainen, and S. Wang, “Challenges of Serendipity in Recommender Systems,” Proceedings of the 12th International Conference on Web Information Systems and Technologies, Vol. 2, 2016, pp. 251-256.

S.M. McNee, J. Riedl, and J. Konstan, “Being Accurate Is not Enough: How Accuracy Metrics Have Hurt Recommender Systems,” CHI '06 Extended Abstracts on Human Factors in Computing Systems, 2006, pp. 1097-1101.

M. de Gemmis, P. Lops, G. Semeraro, and C. Musto, “An Investigation on the Serendipity Problem in Recommender Systems,” Information Processing & Management, Vol. 51, No. 5, pp. 695-717, Sep. 2015.

E. Pariser, The Filter Bubble: What The Internet Is Hiding from You. London, England: Penguin Group, 2011.

J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53, Jan. 2004.

P. Castells, J. Wang, R. Lara, and D. Zhang, “Workshop on Novelty and Diversity in Recommender Systems-DiveRS 2011,” Proceedings of the 5th ACM Conference on Recommender Systems, 2011, pp. 393-394.

N. Hurley and M. Zhang, “Novelty and Diversity in Top-N Recommendation-Analysis and Evaluation,” ACM Transactions on Internet Technology, Vol. 10, No. 4, pp. 1-30, Mar. 2011.

Y.C. Zhang, D.Ó. Séaghdha, D. Quercia, and T. Jambor, “Auralist: Introducing Serendipity into Music Recommendation,” Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 2012, pp. 13-22.

D. Martin. (2008) Most Untranslatable Word. [Online],, access date: Nov. 23, 2018.

L. Iaquinta, M. de Gemmis, P. Lops, G. Semeraro, M. Filannino, and P. Molino, “Introducing Serendipity in a Content-Based Recommender System,” Proceedings of the 8th International Conference on Hybrid Intelligent Systems, 2008, pp. 168-173.

A. Foster and N. Ford, “Serendipity and Information Seeking: An Empirical Study,” Journal of Documentation, Vol. 59, No. 3, pp. 321-340, Jan. 2003.

P. Zhao and D.L. Lee, “How Much Novelty Is Relevant? It Depends on Your Curiosity,” Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016, pp. 315-324.

L. Chen, Y. Yang, N. Wang, K. Yang, and Q. Yuan, “How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation,” Proceedings of the 2019 World Wide Web Conference, 2019, pp. 240-250.

V. Maccatrozzo, M. Terstall, L. Aroyo, and G. Schreiber, “SIRUP: Serendipity in Recommendations via User Perceptions,” Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017, pp. 35-44.

P.J. Silvia, “Emotional Responses to Art: From Collation and Arousal to Cognition and Emotion,” Review of General Psychology, Vol. 9, No. 4, pp. 342-357, Dec. 2005.

T. Murakami, K. Mori, and R. Orihara, “Metrics for Evaluating the Serendipity of Recommendation Lists,” Proceedings of the 2007 Conference on New Frontiers in Artificial Intelligence, 2007, pp. 40-46.

D. Kotkov, S. Wang, and J. Veijalainen, “A Survey of Serendipity in Recommender Systems,” Knowledge-Based Systems, Vol. 111, pp. 180-192, Nov. 2016.

C. Desrosiers and G. Karypis, “A Comprehensive Survey of Neighborhood-based Recommendation Methods,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor, Eds., New York, USA: Springer, 2011, pp. 107-144.

T.D. Pessemier, S. Dooms, and L. Martens, “Comparison of Group Recommendation Algorithms,” Multimedia Tools and Applications, Vol. 72, No. 3, pp. 2497-2541, Oct. 2014.

S. Vargas and P. Castells, “Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems,” Proceedings of the 5th ACM Conference on Recommender Systems, 2011, pp. 109-116.

D. Kotkov, J.A. Konstan, Q. Zhao, and J. Veijalainen, “Investigating Serendipity in Recommender Systems Based on Real User Feedback,” Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1341-1350.

M. Kaminskas and D. Bridge, “Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems,” ACM Transactions on Interactive Intelligent Systems, Vol. 7, No. 1, pp. 1-42, Mar. 2017.

J. Carbonell and J. Goldstein, “The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries,” Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1998, pp. 335-336.

D. Kotkov, J. Veijalainen, and S. Wang, “How Does Serendipity Affect Diversity in Recommender Systems? A Serendipity-Oriented Greedy Algorithm,” Computing, Vol. 102, No. 2, pp. 393-411, Feb. 2020.

S. Sridharan, “Introducing Serendipity in Recommender Systems through Collaborative Methods,” M.S. thesis, University of Rhode Island, Rhode Island, USA, 2014. [Online], https://digi

P. Adamopoulos and A. Tuzhilin, “On Unexpectedness in Recommender Systems,” ACM Transactions on Intelligent Systems and Technology, Vol. 5, No. 4, pp. 1-32, Jan. 2015.

O. Chapelle, D. Metzler, Y. Zhang, and P. Grinspan, “Expected Reciprocal Rank for Graded Relevance,”18th ACM Conference on Information and Knowledge Management, 2009, pp. 621-630.

B. Smyth and P. McClave, “Similarity vs. Diversity,” International Conference on Case-Based Reasoning, 2001, pp. 347-361.

C.N. Ziegler, S.M. McNee, J.A. Konstan, and G. Lausen, “Improving Recommendation Lists through Topic Diversification,” Proceedings of the 14th international conference on World Wide Web, 2005, pp. 22-32.

M.D. Ekstrand, F.M. Harper, M.C. Willemsen, and J.A. Konstan, “User Perception of Differences in Recommender Algorithms,” Proceedings of the 8th ACM Conference on Recommender Systems, 2014, pp. 161-168.

J.P. Kelly and D. Bridge, “Enhancing the Diversity of Conversational Collaborative Recommendations: A Comparison,” Artificial Intelligence Review, Vol. 25, No. 1-2, pp. 79-95, Apr. 2006.

M.T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani, “Pareto-Efficient Hybridization for Multi-Objective Recommender Systems,” Proceedings of the 6th ACM Conference on Recommender Systems, 2012, pp. 19-26.

R.H.B. Christensen, “Ordinal: Regression Models for Ordinal Data,” R Package Version 2015.6-28, access date: Nov. 25, 2018. [Online],


Article Metrics

Abstract views : 1369 | views : 546


  • There are currently no refbacks.

Copyright (c) 2021 IJITEE (International Journal of Information Technology and Electrical Engineering)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ISSN  : 2550-0554 (online)

Contact :

Department of Electrical engineering and Information Technology, Faculty of Engineering
Universitas Gadjah Mada

Jl. Grafika No 2 Kampus UGM Yogyakarta

+62 (274) 552305

Email :