Deep Generative Recommendation Based on List-Wise Ranking

Variational autoencoders have been successfully applied in recommendation field in recent years. The advantage of this kind of nonlinear probabilistic model is that it can break through the limited modeling ability of linear model, which is still dominant in collaborative filtering research. Although the recommendation method based on variational autoencoder has achieved excellent performance, there are still some unresolved problems, such as the inability to generate personalized recommendation ranking lists for users based on the recommendation data of implicit feedback. Therefore, in this paper, we propose a depth generation recommendation model for variational autoencoder by using polynomial likelihood to implement list-based ranking strategies. The model has the ability to simultaneously generate point-wise implicit feedback data and create a list-like ranking list for each user. To seamlessly combine ranking loss with variational autoencoder loss, the normalized cumulative loss gain (NDCG) is adopted here and approximated with a smoothed function. A series of experiments on three real-world datasets (MovieLens-100k, XuetangX and Jester) have been conducted. Experimental results show that the variational autoencoder combined with list-wise ranking method has better performance in generate a personalized recommendation list.