Abstract
Variational Autoencoder (VAE) has been extended as a representative nonlinear latent method for collaborative filtering recommendation. As a high-dimensional representation of data, latent vectors play a vital role in the transmission of important information in a VAE model. However, VAE-based models suffer from a common limitation that the transmission ability of the latent vectors’ important information is limited, resulting in lower quality of global information representation. To address this, we present a novel VAE model with multi-position latent self-attention and reinforcement learning’ actor-critic algorithm. We first build a multi-position latent self-attention model, which can learn richer and more complex latent vectors and strengthens the transmission of important information at different positions. At the same time, we use reinforcement learning to enhance the interactive learning process of collaborative filtering recommendation training. Specifically, our model is stable and can be easy applied in the recommendation. We observed significant improvements over the previous state-of-the-art baselines on three social media datasets, where the largest improvement can reach \(26.10\%\).
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Acknowledgements
This work was partly supported by the Guangzhou Key Laboratory of Big Data and Intelligent Education (No.201905010009) and National Natural Science Foundation of China (No.61672389).
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Feng, J., Liu, M., Hong, S., Song, S. (2023). A Novel Variational Autoencoder with Multi-position Latent Self-attention and Actor-Critic for Recommendation. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_11
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