This paper presents a variational autoencoder (VAE) approach for collaborative filtering with implicit feedback. The authors extend VAEs to collaborative filtering, enabling non-linear probabilistic modeling that surpasses the limitations of linear factor models. They introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. The multinomial likelihood is shown to be well-suited for modeling implicit feedback data and is a closer proxy to ranking loss than more popular likelihood functions like Gaussian and logistic. The authors propose a neural generative model with multinomial conditional likelihood and show that it outperforms several state-of-the-art baselines, including two recently proposed neural network approaches, on several real-world datasets. They also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. The authors identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements. They also introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm have information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. The authors also discuss the computational burden of their approach and propose a taxonomy of autoencoders. They compare their approach with other autoencoder variants, including denoising autoencoders and collaborative denoising autoencoders. They show that their approach, Mult-VAE $ ^{PR} $, performs well on various datasets and outperforms other models in terms of ranking metrics like Recall@R and NDCG@R. They also show that Mult-VAE $ ^{PR} $ is more robust than the point estimate approach of Mult-DAE, regardless of the scarcity of the data. The authors conclude that their approach provides a practical and efficient way to tune the additional parameter introduced using KL annealing and that employing a principled Bayesian approach is more robust. They also show that their approach is less sensitive to the choice of hyperparameters and that Mult-VAE $ ^{PR} $ is more robust than the point estimate approach of Mult-DAE.This paper presents a variational autoencoder (VAE) approach for collaborative filtering with implicit feedback. The authors extend VAEs to collaborative filtering, enabling non-linear probabilistic modeling that surpasses the limitations of linear factor models. They introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. The multinomial likelihood is shown to be well-suited for modeling implicit feedback data and is a closer proxy to ranking loss than more popular likelihood functions like Gaussian and logistic. The authors propose a neural generative model with multinomial conditional likelihood and show that it outperforms several state-of-the-art baselines, including two recently proposed neural network approaches, on several real-world datasets. They also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. The authors identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements. They also introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm have information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. The authors also discuss the computational burden of their approach and propose a taxonomy of autoencoders. They compare their approach with other autoencoder variants, including denoising autoencoders and collaborative denoising autoencoders. They show that their approach, Mult-VAE $ ^{PR} $, performs well on various datasets and outperforms other models in terms of ranking metrics like Recall@R and NDCG@R. They also show that Mult-VAE $ ^{PR} $ is more robust than the point estimate approach of Mult-DAE, regardless of the scarcity of the data. The authors conclude that their approach provides a practical and efficient way to tune the additional parameter introduced using KL annealing and that employing a principled Bayesian approach is more robust. They also show that their approach is less sensitive to the choice of hyperparameters and that Mult-VAE $ ^{PR} $ is more robust than the point estimate approach of Mult-DAE.