This paper introduces a variational autoencoder (VAE) for collaborative filtering, extending the traditional linear factor models to non-linear probabilistic models. The authors propose a generative model with a multinomial likelihood function, which is well-suited for modeling implicit feedback data. They introduce a regularization parameter, β, to partially regularize the VAE, and demonstrate that this approach significantly improves performance compared to standard VAEs. The model is trained using variational inference, and the authors provide an efficient method for tuning the regularization parameter through KL annealing. Empirical results show that the proposed approach outperforms several state-of-the-art baselines on various real-world datasets, including neural network-based approaches. The authors also discuss the pros and cons of the proposed Bayesian inference approach and identify settings where it provides significant improvements.This paper introduces a variational autoencoder (VAE) for collaborative filtering, extending the traditional linear factor models to non-linear probabilistic models. The authors propose a generative model with a multinomial likelihood function, which is well-suited for modeling implicit feedback data. They introduce a regularization parameter, β, to partially regularize the VAE, and demonstrate that this approach significantly improves performance compared to standard VAEs. The model is trained using variational inference, and the authors provide an efficient method for tuning the regularization parameter through KL annealing. Empirical results show that the proposed approach outperforms several state-of-the-art baselines on various real-world datasets, including neural network-based approaches. The authors also discuss the pros and cons of the proposed Bayesian inference approach and identify settings where it provides significant improvements.