Improving Cold-Start Recommendation via Multi-prior Meta-learning

Author:

Chen Zhengyu,Wang Donglin,Yin Shiqian

Publisher

Springer International Publishing

Reference19 articles.

1. Chen, Z., Gai, S., Wang, D.: Deep tensor factorization for multi-criteria recommender systems. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, December 9–12, 2019, pp. 1046–1051 (2019)

2. Chen, Z., Xu, Z., Wang, D.: Deep transfer tensor decomposition with orthogonal constraint for recommender systems. In: The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021 (2021)

3. Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10. ACM (2016)

4. Christakopoulou, E., Karypis, G.: Local latent space models for top-n recommendation. In: 24th ACM SIGKDD, pp. 1235–1243 (2018)

5. Du, Z., Wang, X., Yang, H., Zhou, J., Tang, J.: Sequential scenario-specific meta learner for online recommendation. In: Proceedings of the 25th ACM SIGKDD, pp. 2895–2904 (2019)

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