# Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

arXiv: https://arxiv.org/abs/2201.00014 · HTML: https://mlsanity.com/paper/2201.00014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks](https://mlsanity.com/paper/1404.5772/) (2014)
- [NEXT: A Neural Network Framework for Next POI Recommendation](https://mlsanity.com/paper/1704.04576/) (2017)
- [Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation](https://mlsanity.com/paper/1806.06671/) (2018)

## Abstract (arXiv)

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.