Collaborative Filtering Recommendation Algorithm Based on Improved KMEANS
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4393-3_37
Reference13 articles.
1. Chen, Y., Dai, Y., Han, X., et al.: Dig users’ intentions via attention flow network for personalized recommendation. Inf. Sci. 547, 1122–1135 (2021). https://doi.org/10.1016/j.ins.2020.09.007
2. Zhang, Q., Ren, F.: Double Bayesian pairwise learning for one-class collaborative filtering. Knowl.-Based Syst. 229, 107339 (2021). https://doi.org/10.1016/j.knosys.2021.107339
3. Amer, A.A., Abdalla, H.I., Nguyen, L.: Enhancing recommendation systems performance using highly-effective similarity measures. Knowl.-Based Syst. 217(4), 106842 (2021). https://doi.org/10.1016/j.knosys.2021.106842
4. Yang, E., Huang, Y., Liang, F., et al.: FCMF: federated collective matrix factorization for heterogeneous collaborative filtering. Knowl.-Based Syst. 220(1/2), 106946 (2021). https://doi.org/10.1016/j.knosys.2021.106946
5. Zhou, M.Y., Xu, R.Q., Wang, Z.M., et al.: A generic Bayesian-based framework for enhancing top-N recommender algorithms. Inf. Sci. 580(1) (2021). https://doi.org/10.1016/j.ins.2021.08.048
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