Semantic User Modeling for Personalized News Recommendation and Information Filtering
Keywords:
semantic user modeling; personalized news recommendation; information filtering; large language models; algorithmic governance; fairness; socio-technical infrastructureAbstract
Personalized news recommendation and information filtering have become critical components of contemporary digital information infrastructures. Traditional recommender systems rely heavily on behavioral signals such as clicks, dwell time, and explicit ratings, but these signals alone often fail to capture the dynamic, context-sensitive, and semantically rich nature of user interests. This paper presents a system-level examination of semantic user modeling as a foundation for personalized news recommendation and information filtering. It analyzes the conceptual shift from purely behavioral profiles toward semantically structured representations that incorporate natural language understanding, knowledge organization, and temporal interest evolution. The discussion focuses on architectural dimensions, including candidate retrieval, semantic indexing, memory layers, and re-ranking pipelines, while also addressing infrastructure trade-offs related to latency, scalability, model refresh, and operational sustainability. The paper further examines structural tensions between personalization accuracy and information diversity, the risks of filter bubbles, and the governance challenges arising from opaque language models and user profiling. It argues that semantic user modeling should be treated not only as a computational problem but also as a socio-technical design challenge that requires fairness, accountability, robustness, and policy awareness. By synthesizing cross-domain perspectives from recommender systems, information retrieval, machine learning, and media governance, the paper identifies future directions for building personalized news systems that balance user relevance with broader democratic information access.
References
1. Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer.
2. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.
3. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
4. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
5. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems 30.
7. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171-4186.
8. Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007). Google news personalization: Scalable online collaborative filtering. Proceedings of the 16th International Conference on World Wide Web, 271-280.
9. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp. 325-341). Springer.
10. Okura, S., Tagami, Y., Ono, S., & Tajima, A. (2017). Embedding-based news recommendation for millions of users. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1933-1942.
11. Wu, C., Wu, F., An, M., Huang, J., Huang, Y., & Xie, X. (2019). NPA: Neural news recommendation with personalized attention. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2576-2584.
12. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
13. Karimi, M., Jannach, D., & Jugovac, M. (2018). News recommender systems: Survey and roads ahead. Information Processing & Management, 54(6), 1203-1227.
14. Helberger, N. (2019). On the democratic role of news recommenders. Digital Journalism, 7(8), 993-1012.
15. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin Press.
16. Sunstein, C. R. (2018). #Republic: Divided democracy in the age of social media. Princeton University Press.
17. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21.
18. Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62.
19. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623.
20. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org.
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