A Bibliometric Perspective on Research of Personalized Recommendation Systems and the Filter Bubble Effect in 2024
DOI:
https://doi.org/10.53797/jthkks.v5i2.4.2024Keywords:
Bibliometrics, personalized recommendation systems, information cocoonAbstract
This research adopts a bibliometric approach to investigate the topic of personalized recommendation systems and the information cocoon effect in 2024. Through a systematic review of literature from specific data sources and the application of diverse analytical methods, it provides a detailed examination of the current state and developmental trends in this field. The analysis encompasses the distribution of core authors, prominent journals, and leading countries, highlighting the main research forces in this domain. Co-occurrence and clustering analyses of keywords offer precise insights into research hotspots and emerging directions. This study not only synthesizes the existing research landscape but also identifies pressing challenges and proposes future directions. It aims to offer a comprehensive and detailed reference to support the sustainable development of this field, promote the optimization of personalized recommendation systems, alleviate the information cocoon effect, and contribute to the creation of a healthy, harmonious, and open information society. The findings are of substantial theoretical and practical significance.
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Adomavicius, G., Bauman, K., Tuzhilin, A., & Unger, M. (2021). Context-aware recommender systems: From foundations to recent developments. In Recommender systems handbook (pp. 211-250). New York, NY: Springer US. https://doi.org/10.1007/978-1-0716-2197-4_6
Anwar, M. S., Schoenebeck, G., & Dhillon, P. S. (2024, May). Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns. In Proceedings of the ACM on Web Conference 2024 (pp. 123-134). https://doi.org/10.1145/3589334.3645497
Applied Soft Computing. (2024). Articles and citation metrics. Applied Soft Computing.
Bao, Q., & Tu, D. (2022). Strategies to Solve the Problem of Information Cocoon—Research Progress of Cross-domain Recommendation Algorithm Based on Mining the Potential Interests of Users, pp. 1-9.
Expert Systems with Applications. (2024). Journal performance and metrics. Expert Systems with Applications.
Firoozeh, N., Nazarenko, A., Alizon, F., & Daille, B. (2020). Keyword extraction: Issues and methods. Natural Language Engineering, 26(3), 259-291. https://doi.org/10.1017/S1351324919000457
Gao, H., Ding, X. H., & Wu, S. (2020). Exploring the domain of open innovation: Bibliometric and content analyses. Journal of Cleaner Production, 275, 122580. https://doi.org/10.1016/j.jclepro.2020.122580
Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1-19. https://doi.org/10.1145/2843948
Gu, M., Zhao, T. F., Yang, L., Wu, X. K., & Chen, W. N. (2024). Modeling Information Cocoons in Networked Populations: Insights From Backgrounds and Preferences. IEEE Transactions on Computational Social Systems, 11(3), 4497-4510. https://doi.org/10.1109/TCSS.2024.3354508
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182). https://doi.org/10.1145/3038912.3052569
IEEE Access. (2024a). Research activity metrics and analysis. IEEE Access.
IEEE Access. (2024b). Citation analysis and journal performance. IEEE Access.
Jin, Y., Chen, L., Cai, W., & Zhao, X. (2024). CRS-Que: A user-centric evaluation framework for conversational recommender systems. ACM Transactions on Recommender Systems, 2(1), 1-34. https://doi.org/10.1145/3631534
Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., & Getoor, L. (2020). Generating and understanding personalized explanations in hybrid recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-40. https://doi.org/10.1145/3365843
Lex, E., Kowald, D., Seitlinger, P., Tran, T. N. T., Felfernig, A., & Schedl, M. (2021). Psychology-informed recommender systems. Foundations and trends® in information retrieval, 15(2), 134-242. https://doi.org/10.1561/1500000090
Li, Z., Dong, Y., Gao, C., Zhao, Y., Li, D., Hao, J., ... & Wang, Z. (2023, April). Breaking filter bubble: A reinforcement learning framework of controllable recommender system. In Proceedings of the ACM Web Conference 2023 (pp. 4041-4049). https://doi.org/10.1145/3543507.3583856
Liu, R. (2024). Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm. Entertainment Computing, 51, 100719. https://doi.org/10.1016/j.entcom.2024.100719
Lin, N. (2024). Personalized Book Intelligent Recommendation System Design for University Libraries Based on IBCF Algorithm. IEEE Access, 12, 82015 - 82032. https://doi.org/10.1109/ACCESS.2024.3409752
Lv, L., Kang, K. Q., & Liu, G. (2024). Prick “filter bubbles” by enhancing consumers' novelty‐seeking: The role of personalized recommendations of unmentionable products. Psychology & Marketing, 41(10), 2355-2367. https://doi.org/10.1002/mar.22057
Pu, P., Chen, L., & Hu, R. (2011, October). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 157-164). https://doi.org/10.1145/2043932.2043962
Shi, P., Li, Y., Jiang, X., Shen, Z., Li, R., Lin, Z., ... & Ma, E. (2024). Strong‐Yet‐Ductile Eutectic Alloys Employing Cocoon‐Like Nanometer‐Sized Dislocation Cells. Advanced Materials, 2405459. https://doi.org/10.1002/adma.202405459
Sukiennik, N., Gao, C., & Li, N. (2024, May). Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation. In Proceedings of the ACM on Web Conference 2024 (pp. 4727-4735). https://doi.org/10.1145/3589334.3648159
Wang, M., Hu, Y., Wu, S., Li, W., Bai, Q., Yuan, Z., & Jiang, C. (2024). Nudging Towards Responsible Recommendations: a Graph-Based Approach to Mitigate Belief Filter Bubbles. IEEE Transactions on Artificial Intelligence, pp. 1-15. https://doi.org/10.1109/TAI.2024.3373392
Wu, W., Chen, L., & Zhao, Y. (2018). Personalizing recommendation diversity based on user personality. User Modeling and User-Adapted Interaction, 28(3), 237-276. https://doi.org/10.1007/s11257-018-9205-x.
Yan, C., Pu, K., & Luo, X. (2024). Knowledge mapping of information cocoons: A bibliometric study using visual analysis. Journal of Librarianship and Information Science, 09610006231222628. https://doi.org/10.1177/09610006231222628
Yang, H., Li, D., & Hu, P. (2024). Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity. Technology in Society, 79, 102749. https://doi.org/10.1016/j.techsoc.2024.102749
Yoon, H. Y., You, K. H., Kwon, J. H., Kim, J. S., Rha, S. Y., Chang, Y. J., & Lee, S. C. (2022). Understanding the social mechanism of cancer misinformation spread on YouTube and lessons learned: infodemiological study. Journal of medical Internet research, 24(11), e39571. https://doi.org/10.2196/39571
Zhang, F., Shi, S., Zhu, Y., Chen, B., Cen, Y., Yu, J., ... & Tang, J. (2024, August). Oag-bench: a human-curated benchmark for academic graph mining. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6214-6225). https://doi.org/10.1145/3637528.3672354
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Copyright (c) 2024 Li Linfang, Balamuralithara Balakrishnan
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