An Improved Recommender System for Dealing with Data Sparsity Using Autoencoders and Neural Collaborative Filtering
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-31153-6_18
Reference19 articles.
1. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182, April 2017
2. Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: Fourteenth ACM Conference on Recommender Systems, pp. 240–248, September 2020
3. Liu, X., Wang, Z.: CFDA: collaborative filtering with dual autoencoder for recommender system. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE, July 2022
4. Liu, Y., Wang, S., Khan, M.S., He, J.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018)
5. Ferreira, D., Silva, S., Abelha, A., Machado, J.: Recommendation system using autoencoders. Appl. Sci. 10(16), 5510 (2020)
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