Hyper-parameter Optimization for Latent Spaces-Reference-Cited by-同舟云学术

Hyper-parameter Optimization for Latent Spaces

Published:2021 Issue: Volume: Page:249-264
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases. Research Track
language:
Short-container-title:

Author:

Veloso Bruno,Caroprese Luciano,König Matthias,Teixeira Sónia,Manco Giuseppe,Hoos Holger H.,Gama João

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86523-8_16

Reference41 articles.

1. Agarwal, D., Chen, B.C.: LDA: matrix factorization through latent Dirichlet allocation. In: ACM International Conference on Web Search and Data Mining, pp. 91–100 (2010)

2. Bahri, M., et al.: AutoML for stream k-nearest neighbors classification. In: IEEE International Conference on Big Data, pp. 597–602 (2020)

3. Barbieri, N., Manco, G.: An analysis of probabilistic methods for top-N recommendation in collaborative filtering. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 172–187 (2011)

4. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)

5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

2. Towards adaptive and transparent tourism recommendations: A survey;Expert Systems;2023-07-18

3. Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm;2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS);2022-08-01