Short-Term Memory Variational Autoencoder for Collaborative Filtering.

Recommender systems have been widely used by online service providers to conduct targeted marketing. However, since user behaviour and preferences are fluid and dynamic, predicting users’ online actions is indeed a very difficult task. Variational Autoencoders (VAEs) have been recently proposed to improve the prediction accuracy of user preferences. Although classic VAEs go beyond linear modeling, they suffer from underfitting problem when the underlying datasets are sparse. Moreover, we found that classic VAEs are ineffective for dynamic user preferences. To address these deficiencies, we propose Short Term Memory Variational Autoencoder (STMVAE) to overcome the underfitting issue and to better handle the dynamics. This is achieved by capturing users’ short term preferences to generate near term predictions. The validity and efficacy of our proposed model are evaluated comprehensively using three datasets in our experiments.