An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems
Author:
Funder
Australian Research Council
Publisher
Elsevier BV
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering
Reference54 articles.
1. Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions;Adomavicius;Knowl. Data Eng. IEEE Trans.,2005
2. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions;Adomavicius;Knowl. Data Eng. IEEE Trans.,2005
3. Ahmadian, S., Moradi, P., Akhlaghian, F., 2014. An improved model of trust-aware recommender systems using reliability measurements. In: Proceedings of the 6th IEEE Conference on Information and Knowledge Technology (IKT) 2014. pp. 98–103.
4. Al-Shamri, M.Y.H., Bharadwaj, K.K., 2007. A compact user model for hybrid movie recommender system. In: Proceedings of the IEEE International Conference on Computational Intelligence and Multimedia Applications, 2007. pp. 519–524.
5. Bell, R., Koren, Y., Volinsky, C., 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge discovery and data mining. pp. 95–104.
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