Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (1): 16-35    DOI: 10.11925/infotech.2096-3467.2020.1088
Current Issue | Archive | Adv Search |
Understanding Serendipity in Science: A Survey
Yu Shuo1,Hayat Dino Bedru1,Chu Xinbei1,Yuan Yuyuan1,Wan Liangtian1,Xia Feng2()
1School of Software, Dalian University of Technology, Dalian 116620, China
2School of Engineering, IT and Physical Sciences, Federation University Australia, Ballarat, VIC 3353, Australia
Download: PDF (898 KB)   HTML ( 8
Export: BibTeX | EndNote (RIS)      

[Objective] This paper summarizes the components and definitions of serendipity, reviews representative supporting technologies and applications of serendipity in science, and discusses challenges and future directions in this field. [Coverage] We searched relevant keywords such as “serendipity”, “novelty” and “diversity” in research repositories such as Microsoft Academic and Google Scholar. A total of 102 well-selected references are finally cited. [Methods] We reviewed serendipitous discoveries in various scenarios, and discussed the concept of serendipity in the context of science. Relevant tools and applications are categorized. [Results] The tools that support serendipity are conducive to scientific research. However, there is no uniform definition of serendipity, thus making it difficult to measure serendipity in science. [Limitations] The factors affecting serendipity in science are complex, and yet to be explored. [Conclusions] Serendipity is one of the indispensable factors for scientific advances. However, many challenges are facing the exploration of serendipity in science, such as lack of metrics and difficulty to control.

Key wordsSerendipity in science      Interdisciplinarity      Unexpectedness      Recommender system     
Received: 04 October 2020      Published: 15 December 2020
ZTFLH:  TP393  
Corresponding Authors: Xia Feng     E-mail:

Cite this article:

Yu Shuo,Hayat Dino Bedru,Chu Xinbei,Yuan Yuyuan,Wan Liangtian,Xia Feng. Understanding Serendipity in Science: A Survey. Data Analysis and Knowledge Discovery, 2021, 5(1): 16-35.

URL:     OR

ArticlesDefinition of serendipityFactors
Chakraborti et al. [36]
Wen et al. [37]
McCay-Peet et al. [38]
Maccatrozzo et al. [12]

Kaminskas et al. [39]

Moral et al. [40]

Koesten et al. [41]

Fink et al. [5]
Wang et al. [42]
Copeland [43]

Yaqub [44]
The accidental discovery of something that, post hoc, turns out to be valuable”.
The happy convergence of the mind with conditions”.
The unique and contingent mix of insight coupled with chance”.
a new connection is made that involves a mix of unexpectedness and insight and has the potential to lead to a valuable outcome”.
1) The finding of unexpected information (relevant to the goal or not) while engaged in any information activity; 2) the making of an intellectual leap of understanding with that information to arrive at an insight”.
A method for achieving breadth and identifying information or sources from unknown or partially unknown directions”.
The action of, or aptitude for, encountering relevant information by accident”.
The interactive outcome of unique and contingent “mixes” of insight coupled with chance”.
Falling somewhere between accidental and sagacity,
serendipity is synonymous with neither one nor the other”.
an emergent property of scientific discoveries, describing an oblique relationship between the outcome of a discovery process and the intentions that drove it forward”.
Serendipity may depend on the attributes of the observer and her situation (such as her perceptiveness, instruments and observation systems), or it may depend on the characteristics of the field of inquiry itself (such as when the growth of theory becomes conspicuous for discovery)”.
Chance and positivity
Positivity and mental effort
Chance and insight
Unexpectedness and insight

Unexpectedness and insight


Skill and ability

Insight and chance
Accidental and sagacity
Variation and value

Variety and different forms
Types of toolsSectionSpecific nameReferences
Search Engine3.1MaxBrickley et al. [51]
FeegliRahman et al. [52]
SOL-ToolEichler et al. [53]
LTRC modelHuang et al [54]
Micro-blogging3.2TwitterChen et al.[55], Piao et al. [56], Jiang et al.[57], Kazai et al.[58]
Google BlogLi et al. [59]
Recommender System3.3serendipity-related scholarly papers recommendationSugiyama et al. [60]
serendipity-oriented greedy (SOG) algorithmPradhan et al. [61]
SIRUP modelMaccatrozzo et al. [62]
DESRLi et al. [63]
[1] Xia F, Wang W, Bekele T M, et al.Big Scholarly Data: A Survey[J]. IEEE Transactions on Big Data, 2017, 3(1): 18-35.
[2] Kotkov D, Wang S, Veijalainen J.A Survey of Serendipity in Recommender Systems[J]. Knowledge-Based Systems, 2016, 111: 180-192.
[3] Cook M.Virtual Serendipity: Preserving Embodied Browsing Activity in the 21st Century Research Library[J]. The Journal of Academic Librarianship, 2018, 44(1): 145-149.
[4] Khanna K K.Serendipity, Luck and Hard Work[J]. Nature Cell Biology, 2018, 20(9): 1004.
[5] Fink T M A, Reeves M, Palma R, et al. Serendipity and Strategy in Rapid Innovation[J]. Nature Communications, 2017, 8(1): 1-9.
[6] McCay-Peet L, Toms E G, Kelloway E K. Examination of Relationships Among Serendipity, the Environment, and Individual Differences[J]. Information Processing & Management, 2015, 51(4): 391-412.
[7] Niu X, Abbas F.A Framework for Computational Serendipity[C]// Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. 2017: 360-363.
[8] Walpole H. The Letters of Horace Walpole, Earl of Orford[M]. Lea & Blanchard1844.
[9] Ge X, Daphalapurkar A, Shimpi M, et al.Data-driven Serendipity Navigation in Urban Places[C]//Proceedings of the 37th International Conference on Distributed Computing Systems (ICDCS). 2017: 2501-2504.
[10] Jugovac M, Jannach D, Lerche L.Efficient Optimization of Multiple Recommendation Quality Factors According to Individual User Tendencies[J]. Expert Systems with Applications, 2017, 81: 321-331.
[11] Panahi S, Watson J, Partridge H.Information Encountering on Social Media and Tacit Knowledge Sharing[J]. Journal of Information Science, 2016, 42(4): 539-550.
[12] Maccatrozzo V, van Everdingen E, Aroyo L, et al. Everybody, more or less, likes Serendipity[C]//Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. 2017: 29-34.
[13] Grange C, Benbasat I, Burton-Jones A.With a Little Help from My Friends: Cultivating Serendipity in Online Shopping Environments[J]. Information & Management, 2019, 56(2): 225-235.
[14] Sauer S, de Rijke M. Seeking Serendipity: A Living Lab Approach to Understanding Creative Retrieval in Broadcast Media Production[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016: 989-992.
[15] Liu J, Kong X, Xia F, et al.Artificial Intelligence in the 21st Century[J]. IEEE Access, 2018, 6: 34403-34421.
[16] Yu S, Xia F, Zhang K, et al.Team Recognition in Big Scholarly Data: Exploring Collaboration Intensity[C]//Proceedings of the 3rd IEEE International Conference on Big Data Intelligence and Computing (DataCom). 2017: 925-932.
[17] Stephan P, Veugelers R, Wang J.Reviewers are Blinkered by Bibliometrics[J]. Nature, 2017, 544(7651): 411-412.
[18] Bornmann L, Tekles A, Zhang H H, et al.Do We Measure Novelty When We Analyze Unusual Combinations of Cited References? A Validation Study of Bibliometric Novelty Indicators Based on F1000Prime data[J]. Journal of Informetrics, 2019, 13(4): 100979.
[19] Yu S, Bedru H D, Lee I, et al.Science of Scientific Team Science: A Survey[J]. Computer Science Review, 2019, 31: 72-83.
[20] Wang W, Yu S, Bekele T M, et al.Scientific Collaboration Patterns Vary with Scholars’ Academic Ages[J]. Scientometrics, 2017, 112(1): 329-343.
[21] Singh J, Fleming L.Lone Inventors as Sources of Breakthroughs: Myth or Reality?[J]. Management Science, 2010, 56(1): 41-56.
[22] Perera P, Patel V M.Deep Transfer Learning for Multiple Class Novelty Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 11544-11552.
[23] Niu X, Abbas F, Maher M L, et al.Surprise me if You Can: Serendipity in Health Information[C]//Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018: 1-12.
[24] Yu S, Xia F, Liu H.Academic Team Formulation Based on Liebig’s Barrel: Discovery of Anticask Effect[J]. IEEE Transactions on Computational Social Systems, 2019, 6(5): 1083-1094.
[25] Moirano R, Sánchez M A, Štěpánek L.Creative Interdisciplinary Collaboration: A Systematic Literature Review[J]. Thinking Skills and Creativity, 2020, 35: 100626.
[26] Rafols I, Meyer M.Diversity and Network Coherence as Indicators of Interdisciplinarity: Case Studies in Bionanoscience[J]. Scientometrics, 2010, 82(2): 263-287.
[27] Abramo G, D’Angelo C A, Zhang L, . A comparison of two Approaches for Measuring Interdisciplinary Research Output: The Disciplinary Diversity of Authors vs the Disciplinary Diversity of the Reference List[J]. Journal of Informetrics, 2018, 12(4): 1182-1193.
[28] AlShebli B K, Rahwan T, Woon W L. The Preeminence of Ethnic Diversity in Scientific Collaboration[J]. Nature Communications, 2018, 9(1): 1-10.
[29] Zhou X, Zafarani R.Fake News Detection: An Interdisciplinary Research[C]//Companion Proceedings of The World Wide Web Conference. 2019: 1292-1292.
[30] Kong X, Zhang J, Zhang D, et al.The Gene of Scientific Success[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 14(4): 1-19.
[31] Shneiderman B.Creativity and Collaboration: Revisiting Cybernetic Serendipity[J]. Proceedings of the National Academy of Sciences, 2019, 116(6): 1837-1843.
[32] Valderrama-Zurián J C, Melero-Fuentes D, Aleixandre-Benavent R. Origin, Characteristics, Predominance and Conceptual Networks of Eponyms in the Bibliometric Literature[J]. Journal of Informetrics, 2019, 13(1): 434-448.
[33] Bedru H D, Yu S, Xiao X, et al.Big Networks: A survey[J]. Computer Science Review, 2020, 37: 100247.
[34] Liu J, Tian J, Kong X, et al.Two Decades of Information Systems: A Bibliometric Review[J]. Scientometrics, 2019, 118(2): 617-643.
[35] Ke Q, Ferrara E, Radicchi F, et al.Defining and Identifying Sleeping Beauties in Science[J]. Proceedings of the National Academy of Sciences, 2015, 112(24): 7426-7431.
[36] Chakraborti T, Briggs G, Talamadupula K, et al.Planning for Serendipity[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015: 5300-5306.
[37] Wen H, Ramos Rojas J, Dey A K.Serendipity: Finger Gesture Recognition Using an off-the-shelf Smartwatch[C]//Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016: 3847-3851
[38] McCay‐Peet L, Toms E G. Investigating Serendipity: How it Unfolds and What May Influence it[J]. Journal of the Association for Information Science and Technology, 2015, 66(7): 1463-1476.
[39] Kaminskas M, Bridge D. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-accuracy Objectives in Recommender Systems[J]. ACM Transactions on Interactive Intelligent Systems (TiiS), 2016, 7(1): 1-42.
[40] Moral C, De Antonio A, Ferre X.A visual UML-based Conceptual Model of Information-seeking by Computer Science Researchers[J]. Information Processing & Management, 2017, 53(4): 963-988.
[41] Koesten L M, Kacprzak E, Tennison J F A, et al.The Trials and Tribulations of Working with Structured Data: -a Study on Information Seeking Behaviour[C]//Proceedings of the 2017 SIGCHI Conference on Human Factors in Computing Systems. 2017: 1277-1289.
[42] Wang C D, Deng Z H, Lai J H, et al.Serendipitous Recommendation in e-commerce Using Innovator-based Collaborative Filtering[J]. IEEE Transactions on Cybernetics, 2018, 49(7): 2678-2692.
[43] Copeland S.On Serendipity in Science: Discovery at the Intersection of Chance and Wisdom[J]. Synthese, 2019, 196(6): 2385-2406.
[44] Yaqub O.Serendipity: Towards a Taxonomy and a Theory[J]. Research Policy, 2018, 47(1): 169-179.
[45] Zhuang M, Toms E G, Demartini G.Can User Behaviour Sequences Reflect Perceived Novelty?[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018: 1507-1510.
[46] Cremonini M.Introducing Serendipity in a Social Network Model of Knowledge Diffusion[J]. Chaos, Solitons & Fractals, 2016, 90: 64-71.
[47] Trouille L, Lintott C J, Fortson L F.Citizen Science Frontiers: Efficiency, Engagement, and Serendipitous Discovery with Human-machine Systems[J]. Proceedings of the National Academy of Sciences, 2019, 116(6): 1902-1909.
[48] Allen C M, Erdelez S.Distraction to Illumination: Mining Biomedical Publications for Serendipity in Research[J]. Proceedings of the Association for Information Science and Technology, 2018, 55(1): 10-18.
[49] Xia F, Liu J, Nie H, et al.Random Walks: A Review of Algorithms and Applications[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, 4(2): 95-107.
[50] Liu J, Ren J, Zheng W, et al.Web of Scholars: A Scholar Knowledge Graph[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 2153-2156.
[51] Brickley D, Burgess M, Noy N.Google Dataset Search: Building a Search Engine for Datasets in an Open Web Ecosystem[C]//The World Wide Web Conference. 2019: 1365-1375.
[52] Rahman A, Wilson M L.Exploring Opportunities to Facilitate Serendipity in Search[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015: 939-942.
[53] Eichler J S A, Casanova M A, Furtado A L, et al. Searching Linked Data with a Twist of Serendipity[C]//International Conference on Advanced Information Systems Engineering. 2017: 495-510.
[54] Huang J, Ding S, Wang H, et al.Learning to Recommend Related Entities with Serendipity for web Search Users[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2018, 17(3): 1-22.
[55] Chen J, Nairn R, Nelson L, et al.Short and Tweet: Experiments on Recommending Content from Information Streams[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2010: 1185-1194.
[56] Piao S, Whittle J.A Feasibility Study on Extracting Twitter Users’ Interests Using NLP Tools for Serendipitous Connections[C]//Proceedings of the 3rd International Conference on Privacy, Security, Risk and Trust. 2011: 910-915.
[57] Jiang T, Guo Q, Xu Y, et al.A Diary Study of Information Encountering Triggered by Visual Stimuli on Micro-blogging Services[J]. Information Processing & Management, 2019, 56(1): 29-42.
[58] Kazai G, Yusof I, Clarke D.Personalised News and Blog Recommendations Based on User Location, Facebook and Twitter User Profiling[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016: 1129-1132.
[59] Li F, Du T C.Maximizing Micro-blog Influence in Online Promotion[J]. Expert Systems with Applications, 2017, 70: 52-66.
[60] Sugiyama K, Kan M Y.“Towards Higher Relevance and Serendipity in Scholarly Paper Recommendation” by Kazunari Sugiyama and Min-Yen Kan with Martin Vesely as Coordinator[J]. ACM SIGWEB Newsletter, 2015 (Winter): 1-16.
[61] Pradhan T, Pal S.A Hybrid Personalized Scholarly Venue Recommender System Integrating Social Network Analysis and Contextual Similarity[J]. Future Generation Computer Systems, 2020, 110: 1139-1166.
[62] Maccatrozzo V, Terstall M, Aroyo L, et al.Sirup: Serendipity in Recommendations via User Perceptions[C]//Proceedings of the 22nd International Conference on Intelligent User Interfaces. 2017: 35-44.
[63] Li X, Jiang W, Chen W, et al.Directional and Explainable Serendipity Recommendation[C]//Proceedings of The Web Conference. 2020: 122-132.
[64] Brickley D, Burgess M, Noy N.Google Dataset Search: Building a Search Engine for Datasets in an Open Web Ecosystem[C]//The World Wide Web Conference. 2019: 1365-1375.
[65] Fu C, Peng C, Liu X Y, et al.Search engine: The Social Relationship Driving Power of Internet of Things[J]. Future Generation Computer Systems, 2019, 92: 972-986.
[66] Wang J, Zhang P, Zhang C, et al.SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019: 1309-1312.
[67] Tsurel D, Pelleg D, Guy I, et al.Fun facts: Automatic Trivia Fact Extraction from Wikipedia[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. 2017: 345-354.
[68] Yang S, Pang L, Ngo C W, et al.Serendipity-driven Celebrity Video Hyperlinking[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. 2016: 413-416.
[69] Theisen C, Williams L, Oliver K, et al.Software Security Education at Scale[C]//Proceedings of the 38th International Conference on Software Engineering Companion. 2016: 346-355.
[70] Kong X, Mao M, Wang W, et al.VOPRec: Vector Representation Learning of Papers with Text Information and Structural Identity for Recommendation[J]. IEEE Transactions on Emerging Topics in Computing, 2018:1.
[71] Son J, Kim S B.Academic Paper Recommender System Using Multilevel Simultaneous Citation Networks[J]. Decision Support Systems, 2018, 105: 24-33.
[72] Yang Y, Xu Y, Wang E, et al.Improving Existing Collaborative Filtering Recommendations via Serendipity-based Algorithm[J]. IEEE Transactions on Multimedia, 2017, 20(7): 1888-1900.
[73] Chaiwanarom P, Lursinsap C.Collaborator Recommendation in Interdisciplinary Computer Science Using Degrees of Collaborative Forces, Temporal Evolution of Research Interest, and Comparative Seniority Status[J]. Knowledge-Based Systems, 2015, 75: 161-172.
[74] Jia H, Saule E.An Analysis of Citation Recommender Systems: Beyond the Obvious[C]//Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2017: 216-223.
[75] Xia F, Chen Z, Wang W, et al.MVCWalker: Random Walk-based Most Valuable Collaborators Recommendation Exploiting Academic Factors[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3): 364-375.
[76] Xia F, Liu H, Lee I, et al.Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences[J]. IEEE Transactions on Big Data, 2016, 2(2): 101-112.
[77] Khalili A, Van Andel P, Van Den Besselaar P, et al. Fostering Serendipitous Knowledge Discovery Using an Adaptive Multigraph-based Faceted Browser[C]//Proceedings of the Knowledge Capture Conference. 2017: 1-4.
[78] Kotkov D, Konstan J A, Zhao Q, et al.Investigating Serendipity in Recommender Systems Based on Real User Feedback[C]//Proceedings of the 33rd Annual ACM Symposium on Applied Computing. 2018: 1341-1350.
[79] Cheng P, Wang S, Ma J, et al.Learning to Recommend Accurate and Diverse Items[C]//Proceedings of the 26th International Conference on World Wide Web. 2017: 183-192.
[80] Wang N, Chen L, Yang Y.The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations[C]//Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020: 266-274.
[81] Pandey G, Kotkov D, Semenov A.Recommending Serendipitous Items Using Transfer Learning[C]//Proceedings of the 27th ACM international Conference on Information and Knowledge Management. 2018: 1771-1774.
[82] Lian D, Wang H, Liu Z, et al.LightRec: A Memory and Search-Efficient Recommender System[C]//Proceedings of The Web Conference 2020. 2020: 695-705.
[83] Tseng Y C.PKE: A Model for Recommender Systems in Online Service Platform[C]//Companion Proceedings of the Web Conference 2020. 2020: 289-293.
[84] Kleiner E, Rädle R, Reiterer H.Blended Shelf: Reality-based Presentation and Exploration of Library Collections[M]//Extended Abstracts on Human Factors in Computing Systems. 2013: 577-582.
[85] Hou J, Pan H, Guo T, et al.Prediction Methods and Applications in the Science of Science: A survey[J]. Computer Science Review, 2019, 34: 100197.
[86] Amplayo R K, Hong S L, Song M.Network-based Approach to Detect Novelty of Scholarly Literature[J]. Information Sciences, 2018, 422: 542-557.
[87] Wan L, Yuan Y, Xia F, et al.To Your Surprise: Identifying Serendipitous Collaborators[J]. IEEE Transactions on Big Data, 2019.
[88] Yu W, Cheng W, Aggarwal C C, et al.Netwalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 2672-2681.
[89] Jiang S, Zhang L, Zhang Z.New Collaborative Filtering Algorithm Based on Relative Similarity[J]. Data Analysis and Knowledge Discovery, 2017, 32(12): 44-49.
[90] Jiao F, Li S.Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. Data Analysis and Knowledge Discovery, 2019, 3(8): 62-67.
[91] Alhijawi B, Kilani Y.A Collaborative Filtering Recommender System Using Genetic Algorithm[J]. Information Processing & Management, 2020, 57(6): 102310.
[92] Nozari R B, Koohi H.A Novel Group Recommender System Based on Members’ Influence and Leader Impact[J]. Knowledge-Based Systems, 2020: 106296.
[93] Li X, Jiang W, Chen W, et al.Haes: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1503-1512.
[94] De Gemmis M, Lops P, Semeraro G, et al.An Investigation on the Serendipity Problem in Recommender Systems[J]. Information Processing & Management, 2015, 51(5): 695-717.
[95] Zuva K, Zuva T.Diversity and Serendipity in Recommender Systems[C]//Proceedings of the International Conference on Big Data and Internet of Thing. 2017: 120-124.
[96] Nie H.Modeling Users with Word Vector and Term-Graph Algorithm[J]. Data Analysis and Knowledge Discovery, 2020, 3(12): 30-40.
[97] Xu Y, Yang Y, Han J, et al.Slanderous User Detection with Modified Recurrent Neural Networks in Recommender System[J]. Information Sciences, 2019, 505: 265-281.
[98] Chen J, Jin Q, Zhao S, et al.Boosting Recommendation in Unexplored Categories by User Price Preference[J]. ACM Transactions on Information Systems (TOIS), 2016, 35(2): 1-27.
[99] Wang C D, Deng Z H, Lai J H, et al.Serendipitous Recommendation in E-commerce Using Innovator-based Collaborative Filtering[J]. IEEE Transactions on Cybernetics, 2018, 49(7): 2678-2692.
[100] Tuval N.Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems[C]//Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 2019: 353-356.
[101] Chen L, Yang Y, Wang N, et al.How Serendipity Improves User Satisfaction with Recommendations? A Large-scale User Evaluation[C]//The World Wide Web Conference. 2019: 240-250.
[102] Xu Y, Yang Y, Wang E, et al.Neural Serendipity Recommendation: Exploring the Balance Between Accuracy and Novelty with Sparse Explicit Feedback[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2020, 14(4): 1-25.
[1] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[2] Liu Liu,Wang Dongbo. Identifying Interdisciplinary Social Science Research Based on Article Classification[J]. 数据分析与知识发现, 2018, 2(3): 30-38.
[3] Li Dong,Tong Shouchuan,Li Jiang. Analyzing Interdisciplinarity and Scientists’ Academic Impacts[J]. 数据分析与知识发现, 2018, 2(12): 1-11.
[4] Liu Dan. Personalized Book Recommender Service Deployment Using Apache Mahout[J]. 现代图书情报技术, 2015, 31(10): 102-108.
[5] Tan Xueqing, He Shan. Research Review on Music Personalized Recommendation System[J]. 现代图书情报技术, 2014, 30(9): 22-32.
[6] Xue Fuliang, Zhang Huiying. A Research of Collaborative Filtering Recommender Method Based on SOM and RBFN Filling Missing Values[J]. 现代图书情报技术, 2014, 30(7): 56-63.
[7] Jiang Shuhao, Xue Fuliang. An Improved Content-based Recommendation Method Through Collaborative Predictions and Fuzzy Similarity Measures[J]. 现代图书情报技术, 2014, 30(2): 41-47.
[8] Hu Xinming, Luo Jianjun, Xia Huosong. Research on Interactive Recommender System Based on Commodity Domain Knowledge[J]. 现代图书情报技术, 2014, 30(10): 56-62.
[9] Zhang Huiying, Xue Fuliang. An Improved Collaborative Filtering Recommendation Algorithm Based on Vague Sets Theory[J]. 现代图书情报技术, 2012, 28(3): 35-39.
[10] Zhang Huiying, Xue Fuliang. An Integrated Recommender Method Based on CLV and Collaborative Filtering[J]. 现代图书情报技术, 2012, 28(1): 46-52.
[11] Li Cong. Review of Scalability Problem in E-commerce Collaborative Filtering[J]. 现代图书情报技术, 2010, 26(11): 37-41.
[12] Wang Hongyu,Zhao Ying,Dang Yuewu. Design of an E-commerce Recommender System Based on Hybrid Algorithm[J]. 现代图书情报技术, 2009, 3(1): 80-85.
[13] Ma Wenfeng,Gao Fengrong,Wang Shan. On State Personal Information Service Recommender System in Digital Library[J]. 现代图书情报技术, 2003, 19(2): 16-18.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938