Abstract
Collaborative filtering (CF) is among the most effective techniques for recommendations. However, it suffers from data sparsity and cold-start issue. One solution is to incorporate the side information and the other is to learn knowledge from relevant domains. In this paper, we consider both aspects and propose a generic deep transfer collaborative filtering (DTCF) architecture, which integrates collective matrix factorization and deep transfer learning. We exhibit one instantiation of our architecture by employing non-negative matrix tri-factorization and stacked denoising autoencoder (SDAE) in both source and target domains. Deep learning copes with both the ratings’ statistic characteristics and the side information to generate effective latent representations. Matrix tri-factorization produces private latent factors linked with per SDAE and common latent factors connected with different domains. Extensive experimental results on real datasets exhibit a superiority of our approach in comparison to state-of-the-art works.
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Gai, S., Zhao, F., Kang, Y., Chen, Z., Wang, D., Tang, A. (2019). Deep Transfer Collaborative Filtering for Recommender Systems. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_42
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