Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering. (arXiv:2002.09145v3 [cs.LG] UPDATED)

Matrix factorization (MF) has been widely applied to collaborative filtering
in recommendation systems. Its Bayesian variants can derive posterior
distributions of user and item embeddings, and are more robust to sparse
ratings. However, the Bayesian methods are restricted by their update rules for
the posterior parameters due to the conjugacy of the priors and the likelihood.
Variational autoencoders (VAE) can address this issue by capturing complex
mappings between the posterior parameters and the data. However, current
research on VAEs for …

arxiv attention autoencoder collaborative collaborative filtering feedback filtering lg