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

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 7,385

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

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

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

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

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

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

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 360

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

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 223

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

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

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

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

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

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

Recommender Systems in the Real Estate Market—A Survey

Gharahighehi, A., Pliakos, K., & Vens, C. (2021) · Applied Sciences · doi:10.3390/app11167502 · Cited by 29

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

Carnovalini, F., Rodà, A., & Wiggins, G. A. (2025) · Information · doi:10.3390/info16020151 · Cited by 35

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

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

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

Carrera-Rivera, A., Larrinaga, F., Lasa, G., Martinez-Arellano, G., & Unamuno, G. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09414-0 · Cited by 30

Rethinking Serendipity in Recommender Systems

Kotkov, D., Medlar, A. J., & Głowacka, D. (2023) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3576840.3578310 · Cited by 25

Systematic literature review on identifying influencers in social networks

Seyfosadat, S. F., & Ravanmehr, R. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10515-2 · Cited by 23

Unexpected interest recommender system with graph neural network

Xia, H., Huang, K., & Liu, Y. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00849-9 · Cited by 21

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

Zhang, W., & Li, D. (2022) · Industrial Management & Data Systems · doi:10.1108/imds-04-2022-0209 · Cited by 20

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

Klimashevskaia, A., Elahi, M., Jannach, D., Skjærven, L., Tessem, A., & Trattner, C. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3610637 · Cited by 16

The Dark Matter of Serendipity in Recommender Systems

Kotkov, D., Medlar, A. J., Kask, T., & Głowacka, D. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638342 · Cited by 16

Recommender Systems and Supplier Competition on Platforms

Fletcher, A., Ormosi, P. L., & Savani, R. (2023) · Journal of Competition Law & Economics · doi:10.1093/joclec/nhad009 · Cited by 17

Cognitive-based knowledge learning framework for recommendation

Chen, X., Liang, Q., Chen, Y., Wang, P., Yu, H., & Luo, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111446 · Cited by 16

Modeling Users’ Curiosity in Recommender Systems

Fu, Z., & Niu, X. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3617598 · Cited by 14

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

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

Stitini, O., García‐Magariño, I., Kaloun, S., & Bencharef, O. (2023) · Journal of Advances in Information Technology · doi:10.12720/jait.14.4.701-717 · Cited by 12

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

Jeon, G., Kim, S., & Lee, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2238369 · Cited by 11

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

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 13

Evaluating recommender systems in feature model configuration

Uta, M., Felfernig, A., Le, V.-M., Popescu, A., Tran, T. N. T., & Helić, D. (2021) · International Software Product Line Conference (SPLC) · doi:10.1145/3461001.3471144 · Cited by 10

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

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

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 10

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

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

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

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 14

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

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

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

Xi, Y., Weng, M., Chen, W., Yi, C., Chen, D., Guo, G., Zhang, M., Wu, J., Jiang, Y., Liu, Q., et al. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737199 · Cited by 26

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

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 10

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

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

Cho, M., & Taylor, C. R. (2025) · Journal of Research in Interactive Marketing · doi:10.1108/jrim-06-2025-0312 · Cited by 6

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

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

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 7

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

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 13

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

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

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

Nudges, désinformation et autonomie citoyenne. Une critique de Sunstein

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

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 9

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

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

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

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

(2024) · doi:10.1145/3650105 · Cited by 4

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 2

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

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

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 2

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

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

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

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

Exploring the Power of Weak Ties on Serendipity in Recommender Systems

Jurdi, W. A., Abdo, J. B., Demerjian, J., & Makhoul, A. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-53503-1_17 · Cited by 1

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

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

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

Artificial Intelligence for Consumers

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

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

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

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