Summary of research on recommendation system based on serendipity

doi:10.19682/j.cnki.1005-8885.2022.2022

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (4): 89-105.doi: 10.19682/j.cnki.1005-8885.2022.2022

Summary of research on recommendation system based on serendipity

Meng Wei, Wang Liting, Lu Meng

1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2. Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and
Grassland Administration, Beijing 100083, China

收稿日期:2021-07-02 修回日期:2021-08-18 接受日期:2021-11-14 出版日期:2022-08-31 发布日期:2022-08-31
通讯作者: Meng Wei, E-mail: mnancy@bjfu.edu.cn E-mail:mnancy@bjfu.edu.cn

Summary of research on recommendation system based on serendipity

Meng Wei, Wang Liting, Lu Meng

1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2. Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and
Grassland Administration, Beijing 100083, China

Received:2021-07-02 Revised:2021-08-18 Accepted:2021-11-14 Online:2022-08-31 Published:2022-08-31
Contact: Meng Wei, E-mail: mnancy@bjfu.edu.cn E-mail:mnancy@bjfu.edu.cn

摘要/Abstract

摘要： Personalized recommender systems provide various personalized recommendations for different users through the analysis of their respective historical data. Currently, the problem of the “filter bubble" which has to do with over-specialization persists. Serendipity (SRDP), one of the evaluation indicators, can provide users with unexpected and useful recommendations, and help to successfully mitigate the filter bubble problem, and enhance users' satisfaction levels and provide them with diverse recommendations. Since SRDP is highly subjective and challenging to study, only a few studies have focused on it in recent years. In this study, the research results on SRDP were summarized, the various definitions of SRDP and its applications were discussed, the specific SRDP calculation process from qualitative to quantitative perspectives was presented, the challenges and the development directions were outlined to provide a framework for further research.

关键词: serendipity, recommended algorithm, recommended diversity, filter bubbles, evaluation indicators

Abstract: Personalized recommender systems provide various personalized recommendations for different users through the analysis of their respective historical data. Currently, the problem of the “filter bubble" which has to do with over-specialization persists. Serendipity (SRDP), one of the evaluation indicators, can provide users with unexpected and useful recommendations, and help to successfully mitigate the filter bubble problem, and enhance users' satisfaction levels and provide them with diverse recommendations. Since SRDP is highly subjective and challenging to study, only a few studies have focused on it in recent years. In this study, the research results on SRDP were summarized, the various definitions of SRDP and its applications were discussed, the specific SRDP calculation process from qualitative to quantitative perspectives was presented, the challenges and the development directions were outlined to provide a framework for further research.

Key words: serendipity, recommended algorithm, recommended diversity, filter bubbles, evaluation indicators

中图分类号:

TP391

Meng Wei, Wang Liting, Lu Meng. Summary of research on recommendation system based on serendipity[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 89-105.

参考文献

References
[1] XIANG L. Recommended system practice. Beijing, China: Posts and Telecom Press, 2012 (in Chinese).
[2] PARISER E. The filter bubble: What the Internet is hiding from you. New York, NY, USA: Penguin Press, 2011.
[3] SHANI G, GUNAWARDANA A. Evaluating recommendation systems. RICCI F, ROKACH L, SHAPIRA B, et al (eds).
Recommender systems handbook. Berlin, Germany: Springer, 2011: 257 -297.
[4] KOTKOV D, KONSTAN J A, ZHAO Q, et al. Investigating serendipity in recommender systems based on real user feedback. Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC'18), 2018, Apr 9 -13, Pau, France. New York, NY, USA: ACM, 2018: 1341 -1350.
[5] SCHAFER J B, KONSTAN J, RIEDL J. Recommender systems in e-commerce. Proceedings of the 1st ACM Conference on Electronic Commerce (EC'99), 1999, Nov 3 -5, Denver, CO, USA. New York, NY, USA: ACM, 1999: 158 -166.
[6] YANG B, ZHAO P F. Review the art of recommendation algorithms. Journal of Shanxi University: Natural Science Edition, 2011, 34(3): 337 -350 (in Chinese).
[7] ZHOU T, REN J, MEDO M, et al. Bipartite network projection and personal recommendation. Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 2007, 76(4): 046115.
[8] ANDERSON C. The long tail: why the future of business is selling less of more. New York, NY, USA: Hyperion Press Ltd, 2006.
[9] KOTKOV D, WANG S Q, VEIJALAINEN J. A survey of serendipity in recommender systems. Knowledge-Based Systems, 2016, 111: 180 -192.
[10] CHIU Y S, LIN K H, CHEN J S. A social network-based serendipity recommender system. Proceedings of the 2011
International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS'11), 2011, Dec 7 -9,
Chiang Mai, Thailand. Piscataway, NJ, USA: IEEE, 2011: 1 -5.
[11] ZHANG Y C, SEAGHDHA D O, QUERCIA D, et al. Auralist: Introducing serendipity into music recommendation. Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12), 2012, Feb 8 -12, Seattle, WA, USA. New York, NY, USA: ACM, 2012: 13 -22.
[12] WANG M, KAWAMURA T, SEI Y, et al. Context-aware music recommendation with serendipity using semantic relations. Semantic Technology: Proceedings of the 3rd Joint International Semantic Technology Conference (JIST'13), 2013, Nov 28 -30, Seoul, Republic of Korea. LNISA 8388. Cham, Germany: Springer International Publishing, 2014: 17 -32.
[13] HUANG Z M. Research on recommended system of scholar paper based on topic model. Master Thesis. Dalian, China: Dalian Maritime University, 2013 (in Chinese).
[14] XIAO Z B. Research on ranking topic models and their applications. Ph D Thesis. Dalian, China: Dalian Maritime
University, 2014 (in Chinese).
[15] CHE F. The paper recommender system based on ranking topic model. Graduation Thesis. Dalian, China: Dalian Maritime University, 2015 (in Chinese).
[16] ZHU T. Research on resource recommendation method in knowledge community based on semantic analysis. Master Thesis. Xi'an, China: Xidian University, 2015 (in Chinese).

[17] YI C, ZHOU S S. A study of online consumers' serendipitous product search behavior. Chinese Journal of Management Science, 2016, 24(S1): 329 -336 (in Chinese).
[18] IAQUINTA L, DE GEMMIS M, LOPS P, et al. Introducing serendipity in a content-based recommender system. Proceedings of the 8th International Conference on Hybrid Intelligent Systems, 2008, Sept 10 -12, Barcelona, Spain. Piscataway, NJ, USA: IEEE, 2008: 168 -173.
[19] HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating collaborative filtering recommender systems. ACM
Transactions on Information Systems, 2004, 22(1): 5 -53.
[20] MCNEE S M, RIEDL J, KONSTAN J A. Being accurate is not enough: How accuracy metrics have hurt recommender systems. Proceedings of the CHI 2006 Conference on Human Factors in Computing Systems (CHI'06), 2006, Apr 22 -27, Montreal, Canada. New York, NY, USA: ACM, 2006: 1097 -1101.
[21] GE M, DELGADO-BATTENFELD C, JANNACH D. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. Proceedings of the 4th ACM Conference on Recommender Systems (RecSys'10), 2010, Sept 26 -30,
Barcelona, Spain. New York, NY, USA: ACM, 2010: 257 -260.
[22] OKU K, HATTORI F. User evaluation of fusion-based approach for serendipity-oriented recommender system. Proceedings of the 2012 Workshop on Recommendation Utility Evaluation: Beyond RMSE (RUE'12), 2012, Sept 9 -13, Dublin, Ireland. New York, NY, USA: ACM, 2012: 39 -44.
[23] JENDERS M, LINDHAUER T, KASNECI G, et al. A serendipity model for news recommendation. Advances in
Artificial Intelligence: Proceedings of the 38th Annual German Conference on Artificial Intelligence (KI'15), 2015, Sept 21 -25, Dresden, Germany. LNAI 9324. Cham, Germany: Springer International Publishing, 2015: 111 -123.
[24] SILVA A M, DA SILVA COSTA F H, DIAZ A K R, et al. Exploring coclustering for serendipity improvement in content-based recommendation. Intelligent Data Engineering and Automated Learning: Proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'18): Part I, 2018, Nov 21 -23, Madrid, Spain. LNISA 11314. Cham, Germany: Springer International Publishing, 2018: 317 -327.
[25] YANG Y J, XU Y B, WANG E, et al. Improving existing collaborative filtering recommendations via serendipity-based
algorithm. IEEE Transactions on Multimedia, 2018, 20(7): 1888 -1900.
[26] MATT C, BENLIAN A, HESS T, et al. Escaping from the filter bubble? The effects of novelty and serendipity on users' evaluations of online recommendations. Proceedings of the 35th International Conference on Information Systems (ICIS'14), 2014, Dec 14 -17, Auckland, New Zealand. Darmstadt, Germany: Technical University of Darmstadt, 2014: 1503 -1521.
[27] DE GEMMIS M, LOPS P, SEMERARO G, et al. An investigation on the serendipity problem in recommender systems. Information Processing and Management, 2015, 51(5): 695 -717.
[28] ADAMOPOULOS P, TUZHILIN A. On unexpectedness in recommender systems. ACM Transactions on Intelligent Systems and Technology, 2014, 5(4): 1 -32.
[29] KOTKOV D, VEIJALAINEN J, WANG S. How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing, 2020, 102(2): 393 -411.
[30] MACCATROZZO V, TERSTALL M, AROYO L, et al. SIRUP: Serendipity in recommendations via user perceptions. Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI'17), 2017, Mar 13 -16, Limassol, Cyprus. New York, NY, USA: ACM, 2017: 35 -44.
[31] NIU X. An adaptive recommender system for computational serendipity. Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR'18), 2018, Sept 14 -17, Tianjin China. New York, NY, USA: ACM, 2018: 215 -218.
[32] HUANG J Z, DING S Q, WANG H F, et al. Learning to recommend related entities with serendipity for web search users. ACM Transactions on Asian and Low-Resource Language Information Processing, 2018, 17(3): Article 25.
[33] FOSTER A, FORD N. Serendipity and information seeking: An empirical study. Journal of Documentation, 2003, 59(3): 321 -340.
[34] EKSTRAND M D, RIEDL J T, KONSTAN J A. Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction, 2011, 4(2): 81 -173.
[35] KOTKOV D, VEIJALAINEN J, WANG S Q. Challenges of serendipity in recommender systems. Web Information Systems and Technologies: Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST'16), 2016, Apr 23 -25, Rome, Italy. Lecture Notes in Business Information Processing (LNBIP) 292. Cham, Germany: Springer International Publishing, 2017: 251 -256.
[36] ANDR E P, SCHRAEFEL M C, TEEVAN J, et al. Discovery is never by chance: Designing for (un) serendipity. Proceedings of the 7th ACM Conference on Creativity and Cognition (C&C'09), 2009, Oct 26 -30, Berkeley, CA, USA. New York, NY, USA: ACM, 2009: 305 -314.
[37] CELMA HERRADA O. Music recommendation and discovery in the long tail. Ph D Thesis. Barcelona, Spain: Universitat Pompeu Fabra, 2009.
[38] LU Q X, CHEN T Q, ZHANG W N, et al. Serendipitous personalized ranking for top-n recommendation. Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology: Vol 1, 2012, Dec 4 -7, Macau, China. Piscataway, NJ, USA: IEEE, 2012: 258 -265.
[39] MURAKAMI T, MORI K, ORIHARA R. Metrics for evaluating the serendipity of recommendation lists. New Frontiers in Artificial Intelligence: Proceedings of the 21st Annual Conference of the Japanese Society for Artificial Intelligence (JSAI'07), 2007, Jun 18 -22, Miyazaki, Japan. LNAI 4914. Berlin, Germany: Springer, 2008: 40 -46.
[40] SUGIYAMA K, KAN M Y. Towards higher relevance and serendipity in scholarly paper recommendation. ACM SIGWEB Newsletter, 2015-02-19: Article 4.
[41] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web (WWW'01), 2001, May 1 -5, Hong Kong, China. New York, NY, USA: ACM, 2001: 285 -295.
[42] OKU K, HATTORI F. Fusion-based recommender system for improving serendipity. Proceedings of the 2011 Workshop on Novelty and Diversity in Recommender Systems (DiveRS'11), 2011, Oct 23, Chicago, IL, USA. New York, NY, USA: ACM, 2011: 19 -26.
[43] BERKOVSKY S, FREYNE J. Group-based recipe recommendations: Analysis of data aggregation strategies.
Proceedings of the 4th ACM Conference on Recommender Systems (RecSys'10), 2010, Sept 26 -30, Barcelona, Spain. New York, NY, USA: ACM, 2010: 111 -118.
[44] YAMABA H, TANOUE M, TAKATSUKA K, et al. On a serendipity-oriented recommender system based on folksonomy. Artificial Life and Robotics, 2013, 18(1/2): 89 -94.
[45] ONUMA K, TONG H H, FALOUTSOS C. TANGENT: A novel, “surprise me", recommendation algorithm. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, Jun 28 - Jul 1, Paris, France. New York, NY, USA: ACM, 2009: 657 -666.
[46] KOTKOV D, WANG S Q, VEIJALAINEN J. Improving serendipity and accuracy in cross-domain recommender systems. Web Information Systems and Technologies: Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST'16), 2016, Apr 23 -25, Rome, Italy. Berlin, Germany: Springer, Lecture Notes in Business Information Processing (LNBIP) 292. Cham, Germany: Springer International Publishing, 2017: 105 -119.

Summary of research on recommendation system based on serendipity

Summary of research on recommendation system based on serendipity

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