Analysis Of User Behavior In Information Search Systems Design Of Recommendation Systems
Keywords:
recommender systems, user behavior, information retrieval, collaborative filtering, content-based filtering, implicit data.Abstract
This article examines the issues of analyzing user behavior (clicks, dwell time, search queries, etc.) in information retrieval and recommender systems. The paper analyzes methods for collecting implicit and explicit data, as well as the role of machine learning algorithms in generating personalized recommendations.
References
S.P.Allayorov Axbarot tizimlari Guliston-2020
J.Ernoqulov Axborot kommunikatsiya texnalogiyalarni tizimli tahlil qilish usullari asosida loyihalash. Toshkent-2022
Bobadilla, J., Ortega, F. Hernando, A., & GutiƩrrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132.
Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative Filtering for Implicit Feedback Datasets. IEEE International Conference on Data Mining (ICDM), 263-272.
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30-37.
Zheng, Y., et al. (2018). User Behavior Modeling for Recommendation. ACM Transactions on Information Systems (TOIS), 36(4), 1-34.
7.He, X., et al. (2017). Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web, 173-182.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems Handbook. Springer.
Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer.
Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender Systems: An Introduction. Cambridge University Press.
Smyth, B. (2007). A Personalized Internet: Recommender Systems. AI Magazine, 28(3), 77.