Serendipity in Recommender Systems: A Systematic Literature Review

Item-based collaborative filtering recommendation algorithms

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135 · see citing works

Neural Collaborative Filtering

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052569 · Cited by 7,066 · see citing works

Systematic Literature Reviews in Software Engineering—A Systematic Literature Review

Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537 · see citing works

The psychology of curiosity: A review and reinterpretation.

Loewenstein, G. (1994) · Psychological Bulletin · doi:10.1037/0033-2909.116.1.75 · Cited by 2,738 · see citing works

Guidelines for conducting systematic mapping studies in software engineering: An update

Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2015.03.007 · Cited by 2,669 · see citing works

Recommender Systems: Introduction and Challenges

Ricci, F., Rokach, L., & Shapira, B. (2015) · Recommender Systems Handbook · doi:10.1007/978-1-4899-7637-6_1 · Cited by 894 · see citing works

Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465 · see citing works

A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes, I., & Jannach, D. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9195-0 · Cited by 416 · see citing works

News recommender systems – Survey and roads ahead

Karimi, M., Jannach, D., & Jugovac, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.04.008 · Cited by 337 · see citing works

A survey of serendipity in recommender systems

Kotkov, D., Wang, S., & Veijalainen, J. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.014 · Cited by 334 · see citing works

Shilling attacks against recommender systems: a comprehensive survey

Güneş, İ., Kaleli, C., Bilge, A., & Polat, H. (2012) · Artificial Intelligence Review · doi:10.1007/s10462-012-9364-9 · Cited by 325 · see citing works

Anatomy of the Unsought Finding. Serendipity: Orgin, History, Domains, Traditions, Appearances, Patterns and Programmability

van Andel, P. (1994) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/45.2.631 · Cited by 299 · see citing works

The Private Science of Louis Pasteur

Geison, G. L. (1996) · doi:10.1515/9781400864089 · Cited by 284 · see citing works

Serendipity: Towards a taxonomy and a theory

Yaqub, O. (2017) · Research Policy · doi:10.1016/j.respol.2017.10.007 · Cited by 275 · see citing works

Novelty and Diversity in Recommender Systems

Castells, P., Hurley, N., & Vargas, S. (2015) · Springer eBooks · doi:10.1007/978-1-4899-7637-6_26 · Cited by 273 · see citing works

On Unexpectedness in Recommender Systems

Adamopoulos, P., & Tuzhilin, A. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2559952 · Cited by 233 · see citing works

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

Deng, Z., Huang, L., Wang, C., Lai, J., & Yu, P. S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.330161 · Cited by 194 · see citing works

Recommender Systems: Issues, Challenges, and Research Opportunities

Khusro, S., Ali, Z., & Ullah, I. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-0557-2_112 · Cited by 183 · see citing works

A scientometric review of emerging trends and new developments in recommendation systems

Kim, M. C., & Chen, C. (2015) · Scientometrics · doi:10.1007/s11192-015-1595-5 · Cited by 181 · see citing works

Introducing Serendipity in a Content-Based Recommender System

Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G. M., Filannino, M., & Molino, P. (2008) · International Conference on Hybrid Intelligent Systems (HIS) · doi:10.1109/his.2008.25 · Cited by 181 · see citing works

“Making my own luck”: Serendipity strategies and how to support them in digital information environments

Makri, S., BLANDFORD, A. E., Woods, M., Sharples, S., & Maxwell, D. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23200 · Cited by 164 · see citing works

The value of mapping studies – A participant-observer case study

Kitchenham, B. A., Budgen, D., & Brereton, O. P. (2010) · Electronic workshops in computing · doi:10.14236/ewic/ease2010.4 · Cited by 147 · see citing works

How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation

Chen, L., Yang, Y., Wang, N., Yang, K., & Yuan, Q. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313469 · Cited by 146 · see citing works

A systematic review of scholar context-aware recommender systems

Champiri, Z. D., Shahamiri, S. R., & Salim, S. S. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.09.017 · Cited by 136 · see citing works

An investigation on the serendipity problem in recommender systems

de Gemmis, M., Lops, P., Semeraro, G. M., & Musto, C. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.06.008 · Cited by 134 · see citing works

Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

Wang, C., Deng, Z., Lai, J., & Yu, P. S. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2841924 · Cited by 130 · see citing works

Comparison of group recommendation algorithms

Pessemier, T. D., Dooms, S., & Martens, L. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1563-0 · Cited by 124 · see citing works

User Personality and User Satisfaction with Recommender Systems

Nguyen, T. T., Harper, F. M., Terveen, L., & Konstan, J. A. (2017) · Information Systems Frontiers · doi:10.1007/s10796-017-9782-y · Cited by 118 · see citing works

Dimensions and Metrics for Evaluating Recommendation Systems

Avazpour, I., Pitakrat, T., Grunske, L., & Grundy, J. (2013) · Springer eBooks · doi:10.1007/978-3-642-45135-5_10 · Cited by 108 · see citing works

Investigating serendipity in recommender systems based on real user feedback

Kotkov, D., Konstan, J. A., Zhao, Q., & Veijalainen, J. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167276 · Cited by 100 · see citing works

Three key affordances for serendipity

Björneborn, L. (2017) · Journal of Documentation · doi:10.1108/jd-07-2016-0097 · Cited by 96 · see citing works

Serendipity as an emerging design principle of the infosphere: challenges and opportunities

Reviglio, U. (2019) · Ethics and Information Technology · doi:10.1007/s10676-018-9496-y · Cited by 90 · see citing works

How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm

Kotkov, D., Veijalainen, J., & Wang, S. (2018) · Computing · doi:10.1007/s00607-018-0687-5 · Cited by 87 · see citing works

Examination of relationships among serendipity, the environment, and individual differences

McCay‐Peet, L., Toms, E. G., & Kelloway, E. K. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.02.004 · Cited by 83 · see citing works

With a Little Help from My Friends: Cultivating Serendipity in Online Shopping EnvironmentsPreprint

Grangé, C., Benbasat, I., & Burton‐Jones, A. (2017) · SSRN Electronic Journal · doi:10.1016/j.im.2018.06.001 · Cited by 75 · see citing works

Clustering-based diversity improvement in top-N recommendation

Aytekin, T., & Karakaya, M. Ö. (2013) · Journal of Intelligent Information Systems · doi:10.1007/s10844-013-0252-9 · Cited by 65 · see citing works

The dialectics of serendipity

Cunha, M. P. E., Rego, A., Clegg, S. R., & Lindsay, G. (2014) · European Management Journal · doi:10.1016/j.emj.2014.11.001 · Cited by 65 · see citing works

Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph

Bhandari, U., Sugiyama, K., Datta, A., & Jindal, R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-45068-6_38 · Cited by 49 · see citing works

A systematic literature review of Linked Data‐based recommender systems

Figueroa, C., Vagliano, I., Rocha, O. R., & Morisio, M. (2015) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3449 · Cited by 49 · see citing works

BPAM: Recommendation Based on BP Neural Network with Attention Mechanism

Xi, W.-D., Huang, L., Wang, C., Zheng, Y.-Y., & Lai, J. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/542 · Cited by 47 · see citing works

An Unexpectedness-Augmented Utility Model for Making Serendipitous Recommendation

Zheng, Q., Chan, C.-K., & Ip, H. H. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20910-4_16 · Cited by 43 · see citing works

Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm

Yang, Y., Xu, Y., Wang, E., Han, J., & Yu, Z. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2779043 · Cited by 41 · see citing works

SIRUP

Maccatrozzo, V., Terstall, M., Aroyo, L. M., & Guus Schreiber, A. (2017) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3025171.3025185 · Cited by 39 · see citing works

Group recommender system for store product placement

Park, J., & Nam, K. (2018) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-018-0600-z · Cited by 30 · see citing works

Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system

Manca, M., Boratto, L., & Carta, S. (2015) · Information Systems Frontiers · doi:10.1007/s10796-015-9600-3 · Cited by 29 · see citing works

Identifying Grey Sheep Users in Collaborative Filtering

Gras, B., Brun, A., & Boyer, A. (2016) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/2930238.2930242 · Cited by 29 · see citing works

A Serendipity Model for News Recommendation

Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., & Naumann, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24489-1_9 · Cited by 27 · see citing works

Learning to Recommend Related Entities With Serendipity for Web Search Users

Huang, J., Ding, S., Wang, H., & Liu, T. C. (2018) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3185663 · Cited by 27 · see citing works

Recommending Serendipitous Items using Transfer Learning

Pandey, G., Kotkov, D., & Semenov, A. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3269268 · Cited by 27 · see citing works

Surprise Me If You Can

Niu, X., Abbas, F., Maher, M. L., & Grace, K. S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173597 · Cited by 26 · see citing works

On a Serendipity-oriented Recommender System based on Folksonomy and its Evaluation

Yamaba, H., Tanoue, M., Takatsuka, K., Okazaki, N., & Tomita, S. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.09.104 · Cited by 22 · see citing works

Exploring Opportunities to Facilitate Serendipity in Search

Rahman, A., & Wilson, M. L. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767783 · Cited by 22 · see citing works

Improving collaborative filtering recommender systems using semantic information

Alhijawi, B., Obeid, N., Awajan, A., & Tedmori, S. (2018) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2018.8355454 · Cited by 20 · see citing works

"Towards higher relevance and serendipity in scholarly paper recommendation" by Kazunari Sugiyama and Min-Yen Kan with Martin Vesely as coordinator

Sugiyama, K., & Kan, M. (2015) · ACM SIGWEB Newsletter · doi:10.1145/2719943.2719947 · Cited by 19 · see citing works

Relational social recommendation: Application to the academic domain

Amal, S., Tsai, C.-H., Brusilovsky, P. L., Kuflik, T., & Minkov, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.061 · Cited by 18 · see citing works

Context-Aware Music Recommendation with Serendipity Using Semantic Relations

Wang, M., Kawamura, T., Sei, Y., Nakagawa, H., Tahara, Y., & Ohsuga, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06826-8_2 · Cited by 17 · see citing works

A Framework for Computational Serendipity

Niu, X., & Abbas, F. (2017) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3099023.3099097 · Cited by 17 · see citing works

Identification of Grey Sheep Users by Histogram Intersection in Recommender Systems

Zheng, Y., Agnani, M., & Singh, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69179-4_11 · Cited by 17 · see citing works

A Personalized Location-Based and Serendipity-Oriented Point of Interest Recommender Assistant Based on Behavioral Patterns

Khoshahval, S., Farnaghi, M., Taleai, M., & Mansourian, A. (2018) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-319-78208-9_14 · Cited by 16 · see citing works

User Control and Serendipitous Recommendations in Learning Environments

Afridi, A. H. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.04.032 · Cited by 16 · see citing works

A Scalable Clustering Algorithm for Serendipity in Recommender Systems

Deshmukh, A. A., Nair, P., & Rao, S. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00182 · Cited by 16 · see citing works

Serendipitous Recommendations Through Ontology-Based Contextual Pre-filtering

Karpus, A., Vagliano, I., & Goczyła, K. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-58274-0_21 · Cited by 14 · see citing works

To Your Surprise: Identifying Serendipitous Collaborators

Wan, L., Yuan, Y., Xia, F., & Liu, H. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921567 · Cited by 14 · see citing works

Coupled Topic Model for Collaborative Filtering With User-Generated Content

Wu, S., Guo, W., Xu, S., Huang, Y., Wang, L., & Tan, T. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2016.2586480 · Cited by 13 · see citing works

Private personalized social recommendations in an IPTV system

Elmisery, A. M. (2014) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2014.889222 · Cited by 11 · see citing works

Fostering serendipity in online information systems

Taramigkou, M., Bothos, E., Apostolou, D., & Mentzas, G. N. (2013) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/itmc.2013.7352707 · Cited by 11 · see citing works

Video Recommender System using textual data: Its application on LMS and Serendipity evaluation

Chantanurak, N., Punyabukkana, P., & Suchato, A. (2016) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale.2016.7851809 · Cited by 11 · see citing works

Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

Kotkov, D., Wang, S., & Veijalainen, J. (2017) · Lecture notes in business information processing · doi:10.1007/978-3-319-66468-2_6 · Cited by 11 · see citing works

SEADE Workshop Proposal - The Serendipity Factor

McCay‐Peet, L., Toms, E. G., & Quan‐Haase, A. (2016) · Conference on Human Information Interaction and Retrieval · doi:10.1145/2854946.2878739 · Cited by 10 · see citing works

Accuracy Is Not Enough: Serendipity Should Be Considered More

Yu, H., Wang, Y., Fan, Y., Meng, S., & Huang, R. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-61542-4_22 · Cited by 10 · see citing works

Diversity and Serendipity in Recommender Systems

Zuva, K., & Zuva, T. (2017) · International Conference on Big Data and Internet of Things (BDIOT) · doi:10.1145/3175684.3175694 · Cited by 10 · see citing works

Facilitating research through serendipity of recommendations

Afridi, A. H., Yasar, A.-U.-H., & Shakshuki, E. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01354-7 · Cited by 10 · see citing works

A SERENDIPITOUS RESEARCH PAPER RECOMMENDER SYSTEM

Maake, B. M., Ojo, S. O., & Zuva, T. (2019) · DergiPark (Istanbul University) · Cited by 10 · see citing works

Computational Creativity Based Video Recommendation

Lü, W., & Chung, F.-L. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2914707 · Cited by 9 · see citing works

Towards a Serendipitous Research Paper Recommender System Using Bisociative Information Networks (BisoNets)

Maake, B. M., Ojo, S. O., & Zuva, T. (2018) · doi:10.1109/icabcd.2018.8465475 · Cited by 9 · see citing works

A serendipity-biased Deepwalk for collaborators recommendation

Xu, Z., Yuan, Y., Wei, H., & Wan, L. (2019) · PeerJ Computer Science · doi:10.7717/peerj-cs.178 · Cited by 9 · see citing works

Correlation analysis among the metadata-based similarity, acoustic-based distance, and serendipity of music

Kito, N., Oku, K., & Kawagoe, K. (2014) · International Database Engineering & Applications Symposium (IDEAS) · doi:10.1145/2790755.2790786 · Cited by 8 · see citing works

A New Information Theory-Based Serendipitous Algorithm Design

Zhou, X., Zhan, X., Sun, X. S., & Wang, Q. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58524-6_26 · Cited by 8 · see citing works

Curumim: A Serendipitous Recommender System based on Human Curiosity

dos Santos, A. M., Sebastiá, L., & Ferreira, R. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.098 · Cited by 8 · see citing works

A study on improvement of serendipity in item-based collaborative filtering using association rule

Ito, H., Yoshikawa, T., & Furuhashi, T. (2014) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzz-ieee.2014.6891655 · Cited by 7 · see citing works

Seeking Serendipity

Sauer, S., & de Rijke, M. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2914721 · Cited by 7 · see citing works

Serendipity with Music Streaming Services: The Mediating Role of User and Task Characteristics

Chang, Y.-H., & Tang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78105-1_48 · Cited by 7 · see citing works

Influence of tweets and diversification on serendipitous research paper recommender systems

Nishioka, C., Hauke, J., & Scherp, A. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.273 · Cited by 7 · see citing works

Music recommenders based on hybrid techniques and serendipity

Ziegler, C.-N., Hornung, T., Przyjaciel-Zablocki, M., Gauß, S., & Lausen, G. (2014) · Web Intelligence and Agent Systems An International Journal · doi:10.3233/wia-140294 · Cited by 6 · see citing works

Playlist environmental analysis for the serendipity-based data mining

Wong, C. C., Alias, E. S., & Kishigami, J. (2013) · International Conference on Informatics, Electronics and Vision · doi:10.1109/iciev.2013.6572614 · Cited by 6 · see citing works

Serendipitous Recommenders for Teachers in Higher Education

Afridi, A. H. (2019) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-8476-6.ch017 · Cited by 6 · see citing works

Serendipitous Recommendation Based on Big Context

Koster, A., Koch, F. L., & Kim, Y. B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12027-0_26 · Cited by 5 · see citing works

Searching Linked Data with a Twist of Serendipity

Eichler, J. S. A., Casanova, M. A., Furtado, A. L., Ruback, L., Leme, L. A. P. P., Lopes, G. R., Nunes, B. P., Raffaetà, A., & Renso, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59536-8_31 · Cited by 5 · see citing works

Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors

Qureshi, M. A., & Greene, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71273-4_41 · Cited by 5 · see citing works

Towards Serendipitous Research Paper Recommender Using Tweets and Diversification

Nishioka, C., Hauk, J., & Scherp, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30760-8_29 · Cited by 5 · see citing works

A Personalized and Context-Aware News Offer for Mobile Devices

Pessemier, T. D., Vanhecke, K., & Martens, L. (2016) · Lecture notes in business information processing · doi:10.1007/978-3-319-30996-5_8 · Cited by 4 · see citing works

Emotion Detection Techniques for the Evaluation of Serendipitous Recommendations

de Gemmis, M., Lops, P., & Semeraro, G. M. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-31413-6_17 · Cited by 4 · see citing works

Social Networks Serendipity for Educational Learning by Surprise from Big and Small Data Analysis

Lambropoulos, N., Fardoun, H. M., & Alghazzawi, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58562-8_31 · Cited by 4 · see citing works

FERASAT: A Serendipity-Fostering Faceted Browser for Linked Data

Khalili, A., van den Besselaar, P., & de Graaf, K. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93417-4_23 · Cited by 4 · see citing works

Serendipity-Based Recommendation Framework for SNS Users Using Tie Strength and Relation Clustering

Park, D., Kim, J., & Sohn, M. M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-22263-5_60 · Cited by 4 · see citing works

Visualizing Basic Words Chosen by Latent Dirichlet Allocation for Serendipitous Recommendation

Meng, Q., & Hatano, K. (2014) · IIAI International Conference on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2014.164 · Cited by 3 · see citing works

Bridging the Gap Between User-centric and Offline Evaluation of Personalized Recommendation Systems

Zolaktaf, Z., AlOmeir, O., & Pottinger, R. A. (2018) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3213586.3226216 · Cited by 3 · see citing works

An Effective Approach for Providing Diverse and Serendipitous Recommendations

Jain, I., & Hasija, H. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2757-1_2 · Cited by 2 · see citing works

Item Amalgamation Approach for Serendipity-Oriented Recommender System

Shah, R., Patel, A., & Amin, K. R. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-0135-2_39 · Cited by 2 · see citing works

A survey on popularity bias in recommender systems

Klimashevskaia, A., Jannach, D., Elahi, M., & Trattner, C. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09406-0 · Cited by 179 · see citing works

Benefits of Diverse News Recommendations for Democracy: A User Study

Heitz, L., Lischka, J. A., Birrer, A., Paudel, B., Tolmeijer, S., Laugwitz, L. L., & Bernstein, A. (2022) · Digital Journalism · doi:10.1080/21670811.2021.2021804 · Cited by 100 · see citing works

Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design

Mattis, N. M., Masur, P. K., Möller, J. E., & van Atteveldt, W. (2022) · New Media & Society · doi:10.1177/14614448221104413 · Cited by 78 · see citing works

Recommender systems for sustainability: overview and research issues

Felfernig, A., Wundara, M., Tran, T. N. T., Erdeniz, S. P., Lubos, S., Mansi, M. E., Garber, D., & Le, V.-M. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1284511 · Cited by 66 · see citing works

Knowledge-based recommender systems: overview and research directions

Uta, M., Felfernig, A., Le, V.-M., Tran, T. N. T., Garber, D., Lubos, S., & Burgstaller, T. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1304439 · Cited by 41 · see citing works

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

Fu, Z., Niu, X., & Maher, M. L. (2023) · ACM Computing Surveys · doi:10.1145/3605145 · Cited by 33 · see citing works

Deep neural network approach for a serendipity-oriented recommendation system

Ziarani, R. J., & Ravanmehr, R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115660 · Cited by 32 · see citing works

Recommender Systems in the Real Estate Market—A Survey

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Popularity Bias in Recommender Systems: The Search for Fairness in the Long Tail

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GS$^{2}$-RS: A Generative Approach for Alleviating Cold start and Filter bubbles in Recommender Systems

Xu, Y., Wang, E., Yang, Y., & Xiong, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3290140 · Cited by 29 · see citing works

Recommender Systems in Cybersecurity

Ferreira, L., Silva, D. C., & Uriarte, M. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01906-6 · Cited by 31 · see citing works

AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service Systems

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Rethinking Serendipity in Recommender Systems

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Systematic literature review on identifying influencers in social networks

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Unexpected interest recommender system with graph neural network

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Impact of IT identity on consumer negatively-valenced engagement in mobile medical consultation: from consumer experience perspective

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Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study

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The Dark Matter of Serendipity in Recommender Systems

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Recommender Systems and Supplier Competition on Platforms

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Cognitive-based knowledge learning framework for recommendation

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Modeling Users’ Curiosity in Recommender Systems

Fu, Z., & Niu, X. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3617598 · Cited by 13 · see citing works

GCZRec: Generative Collaborative Zero-Shot Framework for Cold Start News Recommendation

Hassan, S. Z. U., Rafi, M., & Frnda, J. (2024) · IEEE Access · doi:10.1109/access.2024.3359053 · Cited by 13 · see citing works

Towards Ideal and Efficient Recommendation Systems Based on the Five Evaluation Concepts Promoting Serendipity

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Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation System

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A computer vision-based concept model to recommend domestic overseas-like travel experiences: A design science study

Trieu, V.-H., Vu, H. Q., Indulska, M., & Li, G. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114149 · Cited by 11 · see citing works

Agnostic Visual Recommendation Systems: Open Challenges and Future Directions

Podo, L., Prenkaj, B., & Velardi, P. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3374571 · Cited by 10 · see citing works

Evaluating recommender systems in feature model configuration

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Implicit Feedback Awareness for Session Based Recommendation in E-Commerce

Esmeli, R., Bader-El-Den, M. B., Abdullahi, H., & Henderson, D. (2023) · SN Computer Science · doi:10.1007/s42979-023-01752-x · Cited by 9 · see citing works

Demystifying how emotional and cognitive resonance to AI-driven content consistency sparks customer engagement and impulse buying in short-video E-commerce

Nguyen, T. D. X., & Nguyen, K. M. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104646 · Cited by 14 · see citing works

Serendipity on radio and streaming: Between musical discovery and recognition

Kischinhevsky, M., Ferreira, G., & Vieira, I. M. (2023) · New Media & Society · doi:10.1177/14614448231154568 · Cited by 9 · see citing works

Nudging towards diversity: A theoretical framework for facilitating diverse news consumption through recommender designPreprint

Mattis, N. M., Masur, P. K., Möller, J. E., & van Atteveldt, W. (2021) · doi:10.31235/osf.io/wvxf5 · Cited by 9 · see citing works

Consumer resistance to mobile health consultation: the role of IT affordances and IT identity

Zhang, W., Wang, M., & Li, D. (2024) · Aslib Journal of Information Management · doi:10.1108/ajim-01-2024-0036 · Cited by 8 · see citing works

Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems

Sachdeva, T., Goyal, L. M., & Mittal, M. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70006 · Cited by 8 · see citing works

Negotiating the Shared Agency between Humans & AI in the Recommender System

Wu, M., Liu, W., Wang, Y., & Yao, M. Z. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719900 · Cited by 9 · see citing works

What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems

Binst, B., Michiels, L., & Smets, A. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3728325 · Cited by 8 · see citing works

An introduction to collaborative filtering through the lens of the Netflix Prize

Munson, J., Cummins, B., & Zosso, D. P. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-024-02315-z · Cited by 7 · see citing works

Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models

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Systematic literature review on sentiment analysis using transformers

Mostafavi, S., Yahyavi, Y., & Ravanmehr, R. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01021-z · Cited by 7 · see citing works

Encountering Product Information: How Flashes of Insight Improve Your Decisions on E-Commerce Platforms

Wang, L., Zhang, G., & Jiang, D. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19030106 · Cited by 7 · see citing works

Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple modelsPreprint

Shekar, V., Yu, V., Garcia, B. J., Gordon, D. B., Moran, G. E., Blei, D. M., Roch, L. M., García-Durán, A., Najeeb, M. A., Zeile, M., et al. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-l1wpf · Cited by 7 · see citing works

Keeping it fresh: what attributes of AI recommender systems do consumers value?

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Serendipity into session-based recommendation: Focusing on unexpectedness, relevance, and usefulness of recommendations

Boo, S., Kim, S., & Lee, S. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581754.3584138 · Cited by 6 · see citing works

Taming Multi-Output Recommenders for Software Engineering

Treude, C. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3559557 · Cited by 6 · see citing works

The Right to be an Exception to Predictions: a Moral Defense of Diversity in Recommendation Systems

Viganò, E. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00659-y · Cited by 6 · see citing works

Supplier competition on subscription-based platforms in the presence of recommender systemsPreprint

Castellini, J., Fletcher, A., Ormosi, P. L., & Savani, R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4428125 · Cited by 6 · see citing works

Understanding Decentralized Social Feed Curation on Mastodon

Liu, Y., Song, E., Zhang, O. X., Merriman, J., Zhang, L., & Monroy‐Hernández, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757688 · Cited by 5 · see citing works

Augmenting micro-moment recommendations with group and serendipity perspectives

Lin, Y., Zheng, Y.-X., & Ku, Y.-C. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114454 · Cited by 5 · see citing works

Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple modelsPreprint

Shekar, V., Yu, V., Garcia, B. J., Gordon, D. B., Moran, G. E., Blei, D. M., Roch, L. M., García-Durán, A., Najeeb, M. A., Zeile, M., et al. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-l1wpf-v2 · Cited by 5 · see citing works

Avoiding the Overspecialization of Recommender Systems in Tourism with Semantic Trajectories, Initial Thoughts

Bourgais, M., Zanni-Merk, C., Fatali, R., & Alizada, N. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.252 · Cited by 4 · see citing works

Nudges, désinformation et autonomie citoyenne. Une critique de Sunstein

Piguet, J.-G. (2022) · Éthique Publique · doi:10.4000/ethiquepublique.7583 · Cited by 4 · see citing works

A Comprehensive Content-Based Recommendation System for Programming Problems Through Multi-Faceted Code Analysis

Muepu, D. M., Watanobe, Y., & Amin, M. F. I. (2025) · IEEE Access · doi:10.1109/access.2025.3574246 · Cited by 4 · see citing works

PUB-VEN: a personalized recommendation system for suggesting publication venues

Ajmal, S., Sarfraz, M. S., Memon, I., Bilal, M., & Alam, K. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16798-5 · Cited by 4 · see citing works

The Use of a Genetic Algorithm to Alleviate the Limited Content Issue in a Content-Based Recommendation System

Stitini, O., Kaloun, S., & Bencharef, O. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26254-8_112 · Cited by 4 · see citing works

Fusion-based Music Recommender System Using Music Affective Space based on Serendipity

Saito, T., Sato-Shimokawara, E., & Chen, L.-H. (2022) · Conference on Technologies and Applications of Artificial Intelligence (TAAI) · doi:10.1109/taai57707.2022.00039 · Cited by 4 · see citing works

Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering

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Sustainability Evaluation Metrics for Recommender Systems

Felfernig, A., Garber, D., Le, V.-M., Lubos, S., & Tran, T. N. T. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-13342-7_2 · Cited by 3 · see citing works

Offline evaluation of the serendipity in recommendation systems

Pastukhov, D., Kuznetsov, S., Vančura, V., & Kordík, P. (2022) · IEEE International Conference on Computer Sciences and Information Technologies (CSIT) · doi:10.1109/csit56902.2022.10000782 · Cited by 3 · see citing works

Designing Artificial Serendipity

Chen, X., Lin, A. S., & Webber, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60405-8_3 · Cited by 3 · see citing works

Review of User Interface-Facilitated Serendipity in Recommender Systems

Afridi, A. H., & Olsson, T. (2023) · International Journal of Interactive Communication Systems and Technologies · doi:10.4018/ijicst.320180 · Cited by 3 · see citing works

Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems

Nalis, I., Sippl, T., Kolb, T. E., & Neidhardt, J. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664901 · Cited by 3 · see citing works

Can Large Language Models Assess Serendipity in Recommender Systems?

Tokutake, Y., Editorial Office, & Okamoto, K. (2024) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2024.p1263 · Cited by 3 · see citing works

Revisión conceptual de sistemas de recomendación y geolocalización aplicados a la seguridad turística

Solano-Barliza, A. D. (2021) · Computer and Electronic Sciences Theory and Applications · doi:10.17981/cesta.02.02.2021.05 · Cited by 3 · see citing works

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

Sun, N. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/6/20230861 · Cited by 3 · see citing works

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks

Sasai, K., Fukutani, R., Kitagata, G., & Kinoshita, T. (2022) · Sensors · doi:10.3390/s22228841 · Cited by 2 · see citing works

Fairness and Bias in E-commerce Recommendation Systems: A Literature Review

Irena, A. M., Wahyudi, A. N., Wairooy, I. K., & Makalew, B. A. (2024) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis63449.2024.10956674 · Cited by 2 · see citing works

Retrieval Augmented Generation Model for Paper Recommendation System

Yadav, N., & Gopinathan, D. (2025) · SN Computer Science · doi:10.1007/s42979-025-04095-x · Cited by 2 · see citing works

Uncovering Recommendation Serendipity with Objective Data-driven Factor Investigation

Jiang, W., Li, S., Li, X., Li, K., & Wu, J. (2025) · ACM Transactions on Information Systems · doi:10.1145/3758092 · Cited by 2 · see citing works

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems

Kang, L., Zhao, Y., & Chen, L. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748167 · Cited by 2 · see citing works

Democracy in the Time of “Hyperlead”: Knowledge Acquisition via Algorithmic Recommendation and Its Political Implication in Comparison with Orality, Literacy, and Hyperlink

Son, W.-C. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00573-9 · Cited by 2 · see citing works

Serendipity-Oriented Clustering Method for Recommender Systems Based on Collaborative Filtering

Şenyürek, E., & Kevrić, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-17697-5_35 · Cited by 2 · see citing works

How to Evaluate Serendipity in Recommender Systems: the Need for a Serendiptionnaire

Binst, B. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688017 · Cited by 2 · see citing works

A personalized content-based method to predict customers’ preferences in an online apparel retailer

KabirMamdouh, A., & Kök, A. G. (2024) · International Journal of Production Economics · doi:10.1016/j.ijpe.2024.109487 · Cited by 2 · see citing works

Existence Conditions for Hidden Feedback Loops in Online Recommender Systems

Khritankov, A., & Pilkevich, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91560-5_19 · Cited by 2 · see citing works

Serendipity-Oriented Recommender System with Dynamic Unexpectedness Prediction

Tokutake, Y., & Okamoto, K. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394368 · Cited by 2 · see citing works

NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

Li, X., Wang, M., Wang, C., Fu, Y., & Wang, X. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150366 · Cited by 2 · see citing works

Estimating serendipity in content-based recommender systemsPreprint

Hertz, A., Kuflik, T., & Tuval, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3090614/v1 · Cited by 2 · see citing works

Navigating the Landscape: A Comprehensive Survey on Healthcare Recommendation Systems

Gupta, M., Singhal, S., & Tomar, A. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-5200-3_32 · Cited by 1 · see citing works

The Knowledge Commons in the Age of AI: Opportunities and Risks for Urban Smart Learning

Lister, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92980-9_4 · Cited by 1 · see citing works

Balancing Accuracy and Novelty with Sub-Item Popularity

Mallamaci, C., Petrov, A. V., Mancino, A. C. M., Anelli, V. W., Noia, T. D., & Macdonald, C. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3759311 · Cited by 1 · see citing works

The differential effects of algorithmic recommendations on user experience: Enjoyment and serendipity in everyday music streaming

Dietrich, F., Ernst, A., Kreling, R., Gilbert, A., Rohr, B., Scharkow, M., & Reinecke, L. (2025) · International Conference of the ACM Greek SIGCHI Chapter (CHIGreece) · doi:10.1145/3749012.3749040 · Cited by 1 · see citing works

Bisociative Pivoting as a Method for Moderating User Response to Text Similarity in Next-item News Recommendation

Kiddle, R., Welbers, K., Kroon, A. C., & Trilling, D. (2026) · ACM Transactions on Recommender Systems · doi:10.1145/3808221 · Cited by 1 · see citing works

Sustainability-aware evaluation metrics for recommender systems

Felfernig, A., Garber, D., Helić, D., Le, V.-M., Lubos, S., & Tran, T. N. T. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01045-8 · Cited by 1 · see citing works

Generative AI as a Scaffold in Travel Decision-Making: A Conceptual Framework

Kim, Y., Tussyadiah, I. P., & Kim, B. (2026) · Journal of Travel Research · doi:10.1177/00472875261484157 · Cited by 1 · see citing works

AI-Driven Personalization and Brand Loyalty: Perceived Recommendation Discovery and Perceived AI Recommendation Quality on Smart Tourism Platforms

Abdelhamied, H. H. S., & Al‐Romeedy, B. S. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21100348 · Cited by 1 · see citing works

An Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity

Uppal, A., Maitreyi, P., Shreya, P., Jain, T., & Das, B. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35299-7_3 · Cited by 1 · see citing works

A Comprehensive Study on Online and Offline Evaluation of Recommendation System

Sachdeva, T., Goyal, L. M., & Mittal, M. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-4626-6_39 · Cited by 1 · see citing works

Development of a hybrid information recommendation system considering serendipity

Domoto, H., Uchiya, T., & Takumi, I. (2023) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce59613.2023.10315407 · Cited by 1 · see citing works

Exploring the Power of Weak Ties on Serendipity in Recommender Systems

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Creative and Correct: Requesting Diverse Code Solutions from AI Foundation Models

Blyth, S., Treude, C., & Wagner, M. (2024) · IEEE/ACM International Conference on AI Foundation Models and Software Engineering (FORGE) · doi:10.1145/3650105.3652302 · Cited by 1 · see citing works

Personalisation in Mobility-as-a-Service: Where We Are and How to Move Forward

Oberoi, K. S. (2024) · International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) · doi:10.5220/0012687100003702 · Cited by 1 · see citing works

Serendipität und Open Data. Richtlinien für die Implementierung von Empfehlungssystemen auf Entdeckungsplattformen

Bertino, A. C. (2024) · BIBLIOTHEK Forschung und Praxis · doi:10.1515/bfp-2023-0059 · Cited by 1 · see citing works

Artificial Intelligence for Consumers

Portinale, L., & Abluton, A. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009483599.005 · Cited by 1 · see citing works

Optimizing Diverse Information Exposure in Social Graphs

Colin, J., & Maniu, S. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825032 · Cited by 1 · see citing works

Understanding the Usages and Effects of a Recommendation System in a Non-formal Learning Context

Badier, A., Lefort, M., & Lefèvre, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32883-1_5

Two-sided fairness-aware recommendation method under many-to-many relation schema

Zhang, L., & Nie, B. (2023) · International Conference on Information Science, Electrical and Automation Engineering (ISEAE) · doi:10.1117/12.2689461

Learning to Rank in Session-Based Recommender Systems

Ravanmehr, R., & Mohamadrezaei, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-42559-2_6

Rise of AI Consumer Markets

DiMatteo, L. A., Poncibó, C., & Howells, G. G. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009483599.003

Fog Computing in Healthcare: Enhancing Recommendations and Real-Time Monitoring

Salih, A. F., & AL-Bakri, N. F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6106-7_14

Improving User Satisfaction Through Approaches that Balance Recommendation Accuracy and Serendipity Tailored to Individual Preferences

Domoto, H., Uchiya, T., & Takumi, I. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0026-7_6

A Serendipitous Recommendation System Considering User Curiosity

Xu, Z., & Matsumura, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78093-6_3

A survey of serendipity in recommender systems

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Evaluating the Quality of Recommendation System by Using Serendipity Measure

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Computational Serendipitous Recommender System Frameworks: A Literature Survey

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A Serendipity-Oriented Greedy Algorithm for Recommendations

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Challenges of Serendipity in Recommender Systems

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