# Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors

arXiv: https://arxiv.org/abs/2006.06820 · HTML: https://mlsanity.com/paper/2006.06820/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 12 of 12)

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- [Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems](https://mlsanity.com/paper/2108.05385/) (2021)
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- [A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality](https://mlsanity.com/paper/2204.13842/) (2022)
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- [Investigating Cross-Domain Behaviors of BERT in Review Understanding](https://mlsanity.com/paper/2306.15123/) (2023)
- [Embedding Mental Health Discourse for Community Recommendation](https://mlsanity.com/paper/2307.03892/) (2023)
- [From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction](https://mlsanity.com/paper/2410.05323/) (2024)

## Abstract (arXiv)

User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent behavior log as a sequence of adopted items and find sequential patterns; however, concrete location and time information in the behavior log, reflecting dynamic and periodic patterns, joint with the spatial dimension, can be useful for modeling users and predicting their characteristics. In this work, we propose a novel model based on graph neural networks for learning user representations from spatiotemporal behavior data. A behavior log comprises a sequence of sessions; and a session has a location, start time, end time, and a sequence of adopted items. Our model's architecture incorporates two networked structures. One is a tripartite network of items, sessions, and locations. The other is a hierarchical calendar network of hour, week, and weekday nodes. It first aggregates embeddings of location and items into session embeddings via the tripartite network, and then generates user embeddings from the session embeddings via the calendar structure. The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It adopts the attention mechanism to model complex interactions among the multiple patterns in user behaviors. Experiments on real datasets (i.e., clicks on news articles in a mobile app) show our approach outperforms strong baselines for predicting missing demographic attributes.