  Temporal set prediction is becoming increasingly important as many companies
employ recommender systems in their online businesses, e.g., personalized
purchase prediction of shopping baskets. While most previous techniques have
focused on leveraging a user's history, the study of combining it with others'
histories remains untapped potential. This paper proposes Global-Local Item
Embedding (GLOIE) that learns to utilize the temporal properties of sets across
whole users as well as within a user by coining the names as global and local
information to distinguish the two temporal patterns. GLOIE uses Variational
Autoencoder (VAE) and dynamic graph-based model to capture global and local
information and then applies attention to integrate resulting item embeddings.
Additionally, we propose to use Tweedie output for the decoder of VAE as it can
easily model zero-inflated and long-tailed distribution, which is more suitable
for several real-world data distributions than Gaussian or multinomial
counterparts. When evaluated on three public benchmarks, our algorithm
consistently outperforms previous state-of-the-art methods in most ranking
metrics.
