# Global-Local Item Embedding for Temporal Set Prediction

arXiv: https://arxiv.org/abs/2109.02074 · HTML: https://mlsanity.com/paper/2109.02074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [Modeling Personalized Item Frequency Information for Next-basket Recommendation](https://mlsanity.com/paper/2006.00556/) (2020)
- [Predicting Temporal Sets with Deep Neural Networks](https://mlsanity.com/paper/2006.11483/) (2020)
- [Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models](https://mlsanity.com/paper/1508.06378/) (2015)
- [Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation](https://mlsanity.com/paper/1908.02569/) (2019)

## Built on by (most cited first, 1 of 1)

- [Intent-based Product Collections for E-commerce using Pretrained Language Models](https://mlsanity.com/paper/2110.08241/) (2021)

## Abstract (arXiv)

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.