# PG2Net: Personalized and Group Preferences Guided Network for Next Place Prediction

arXiv: https://arxiv.org/abs/2110.08266 · HTML: https://mlsanity.com/paper/2110.08266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification](https://mlsanity.com/paper/2004.05707/) (2020)
- [DeepTrax: Embedding Graphs of Financial Transactions](https://mlsanity.com/paper/1907.07225/) (2019)
- [Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach](https://mlsanity.com/paper/1907.03381/) (2019)
- [Evidence for a Conserved Quantity in Human Mobility](https://mlsanity.com/paper/1609.03526/) (2016)
- [NEXT: A Neural Network Framework for Next POI Recommendation](https://mlsanity.com/paper/1704.04576/) (2017)
- [Collective benefits in traffic during mega events via the use of information technologies](https://mlsanity.com/paper/1607.08203/) (2016)

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

- [Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network](https://mlsanity.com/paper/2210.07765/) (2022)

## Abstract (arXiv)

Predicting the next place to visit is a key in human mobility behavior modeling, which plays a significant role in various fields, such as epidemic control, urban planning, traffic management, and travel recommendation. To achieve this, one typical solution is designing modules based on RNN to capture their preferences to various locations. Although these RNN-based methods can effectively learn individual's hidden personalized preferences to her visited places, the interactions among users can only be weakly learned through the representations of locations. Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG2Net), considering the users' preferences to various places at both individual and collective levels. Specifically, PG2Net concatenates Bi-LSTM and attention mechanism to capture each user's long-term mobility tendency. To learn population's group preferences, we utilize spatial and temporal information of the visitations to construct a spatio-temporal dependency module. We adopt a graph embedding method to map users' trajectory into a hidden space, capturing their sequential relation. In addition, we devise an auxiliary loss to learn the vectorial representation of her next location. Experiment results on two Foursquare check-in datasets and one mobile phone dataset indicate the advantages of our model compared to the state-of-the-art baselines. Source codes are available at https://github.com/urbanmobility/PG2Net.