Overview of definition, evaluation, and algorithms of serendipity in recommender systems

Research Article
Open access

Overview of definition, evaluation, and algorithms of serendipity in recommender systems

Ning Sun 1*
  • 1 University of Alberta, Edmonton, Canada, T6G 2R3    
  • *corresponding author ning2@ualberta.ca
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230861
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Over time, recommendation systems are playing an important role in an increasingly wide range of areas, such as paper retrieval sites that can recommend papers or books to users, and shopping sites that can recommend products to users. With the development of recommendation systems, there are many different metrics to measure a good recommendation system, including serendipity. This paper summarizes the definition of serendipity, a review of the metrics for measuring serendipity, and several major serendipity-oriented algorithms and presents conjectures for future research on serendipity. Through the research of some papers, for how to delimit and evaluate recommender systems, experts have mostly focused on the unexpected, and most of them use and optimize collaborative filtering algorithms to achieve and improve serendipity.

Keywords:

recommender system, serendipity, systematic literature review, content-based filtering, collaborative filtering, greedy algorithm.

Sun,N. (2023). Overview of definition, evaluation, and algorithms of serendipity in recommender systems. Applied and Computational Engineering,6,460-466.
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References

[1]. Kotkov, D., Veijalainen, J. & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing 102, 393–411.

[2]. Ziarani, R. J., & Ravanmehr, R. (2021). Serendipity in recommender systems: a systematic literature review. Journal of Computer Science and Technology, 36(2), 375-396.

[3]. Toms, E. G. (2000). Serendipitous Information Retrieval. DELOS Workshop: Information Seeking, Searching and Querying in Digital Libraries.

[4]. Kaminskas, M., & Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(1), 1-42.

[5]. Kotkov, D., Konstan, J. A., Zhao, Q., & Veijalainen, J. (2018, April). Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the 33rd annual acm symposium on applied computing (pp. 1341-1350).

[6]. Zheng, Q., Chan, C. K., & Ip, H. H. (2015, July). An unexpectedness-augmented utility model for making serendipitous recommendation. In Industrial conference on data mining(pp. 216- 230). Springer, Cham.

[7]. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192.

[8]. Niu, X., & Abbas, F. (2017, July). A framework for computational serendipity. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 360-363).

[9]. Afridi, A. H. (2018). User control and serendipitous recommendations in learning environments. Procedia computer science, 130, 214-221.

[10]. Lops, P., Gemmis, M. D., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, 73-105.

[11]. Saat, Nur Izyan Yasmin, Shahrul Azman Mohd Noah and Masnizah Mohd. “Towards Serendipity for Content–Based Recommender Systems.” International Journal on Advanced Science, Engineering and Information Technology (2018): n. pag.

[12]. Kotkov, D., Wang, S., & Veijalainen, J. (2016, April). Improving serendipity and accuracy in cross-domain recommender systems. In International Conference on Web Information Systems and Technologies (pp. 105-119). Springer, Cham.

[13]. Nguyen, T. T., Maxwell Harper, F., Terveen, L., & Konstan, J. A. (2018). User personality and user satisfaction with recommender systems. Information Systems Frontiers, 20(6), 1173-1189.

Cite this article

Sun,N. (2023). Overview of definition, evaluation, and algorithms of serendipity in recommender systems. Applied and Computational Engineering,6,460-466.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Kotkov, D., Veijalainen, J. & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing 102, 393–411.

[2]. Ziarani, R. J., & Ravanmehr, R. (2021). Serendipity in recommender systems: a systematic literature review. Journal of Computer Science and Technology, 36(2), 375-396.

[3]. Toms, E. G. (2000). Serendipitous Information Retrieval. DELOS Workshop: Information Seeking, Searching and Querying in Digital Libraries.

[4]. Kaminskas, M., & Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(1), 1-42.

[5]. Kotkov, D., Konstan, J. A., Zhao, Q., & Veijalainen, J. (2018, April). Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the 33rd annual acm symposium on applied computing (pp. 1341-1350).

[6]. Zheng, Q., Chan, C. K., & Ip, H. H. (2015, July). An unexpectedness-augmented utility model for making serendipitous recommendation. In Industrial conference on data mining(pp. 216- 230). Springer, Cham.

[7]. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192.

[8]. Niu, X., & Abbas, F. (2017, July). A framework for computational serendipity. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 360-363).

[9]. Afridi, A. H. (2018). User control and serendipitous recommendations in learning environments. Procedia computer science, 130, 214-221.

[10]. Lops, P., Gemmis, M. D., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Recommender systems handbook, 73-105.

[11]. Saat, Nur Izyan Yasmin, Shahrul Azman Mohd Noah and Masnizah Mohd. “Towards Serendipity for Content–Based Recommender Systems.” International Journal on Advanced Science, Engineering and Information Technology (2018): n. pag.

[12]. Kotkov, D., Wang, S., & Veijalainen, J. (2016, April). Improving serendipity and accuracy in cross-domain recommender systems. In International Conference on Web Information Systems and Technologies (pp. 105-119). Springer, Cham.

[13]. Nguyen, T. T., Maxwell Harper, F., Terveen, L., & Konstan, J. A. (2018). User personality and user satisfaction with recommender systems. Information Systems Frontiers, 20(6), 1173-1189.

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