Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

By Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou. AAAI Conference on Artificial Intelligence, 2019

478 citations recorded by the source corpus.

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

Subfields: Artificial Intelligence, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/697fc0da53bf06cded346f961390418ba9fea23c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tong Chen
2. Fuzhen Zhuang
3. Hui Xiong
4. Qing He
5. Hengshu Zhu
6. Chuan Qin
7. Qingyu Guo
8. Xing Xie

In Information Systems

1. Tong Chen
2. Xing Xie
3. Fuzhen Zhuang
4. Hui Xiong
5. Qing He
6. Hengshu Zhu
7. Chuan Qin
8. Qingyu Guo

In Transportation

1. Hui Xiong
2. Xing Xie
3. Qiang Liu
4. Hongzhi Yin
5. Yu Zheng
6. Ouri Wolfson
7. Jing Yuan
8. Mohamed F. Mokbel

Citations in the corpus

• Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation (2020, methodology): “As a recent attempt, STGN (Zhao et al. 2019) presents a gated mechanism to model both long-and short-term interests, which is also under the LSTM architecture.”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, background): “Recommender systems have been applied in many domains, including movies [2], [44], music [1], [45], POIs [46], [47], news [14], [48], education [49], [50], etc.”
• STAN: Spatio-Temporal Attention Network for Next Location Recommendation (2021, background): “• STGN [43]: a state-of-the-art model that adds time and distance interval gates to LSTM.”