shawnsu007/an-attention-based-spatiotemporal-lstm-network-for-next-poi-recommendation

A python vision code of An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation

An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation

Next POI (Point-of-Interest) recommendation, also known as a natural extension of general POI recommendation, is recently proposed to predict user’s next destination and has attracted considerable research interest. It focuses on learning users’ sequential patterns of check-in behavior and training personalized recommendation models using different types of contextual information. Unfortunately, most of the previous studies failed to incorporate the spatiotemporal contextual information, which plays a critical role in analyzing user check-in behavior, into next POI recommendation. In recent years, embedding learning and RNN (Recurrent Neural Network) based approaches show promising performance for modeling sequential patterns of check-in behavior in next POI recommendation. However, not all of the historical check-in records contribute equally to the next-step check-in behavior. To provide better next POI recommendation performance, we first propose a spatiotemporal long and short-term memory (ST-LSTM) network. By feeding the spatiotemporal contextual information into the LSTM network in each step, ST-LSTM can model the spatial and temporal information better. Also, we develop an attention-based spatiotemporal LSTM (ATST-LSTM) network for next POI recommendation. By using the attention mechanism, ATST-LSTM can focus on the related historical check-ins in a check-in sequence selectively using the spatiotemporal contextual information. Please refer to our paper An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation for further details.

Next, we introduce how to run our model for provided example data or your own data.

Environment

Python 3.5

TensorFlow 1.4.1

Numpy 1.15.0

Usage

As an illustration, we provide the data and running command for Gowalla and Brightkite.

Input data

userlocation.csv：includes user ID, POI ID, latitude, longitude, checkin time.

locations.csv: includes POI ID, latitude, longitude, city ID

The original dataset can be downloaded from http://snap.stanford.edu/data/, but we need to preprocess the dataset according to 5.2 in our paper

Contact

Liwei Huang, [email protected]

Citation

If you use AT-LSTM in your research, please cite our paper:

Liwei Huang, Yutao Ma, Shibo Wang,Yanbo Liu. An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation. IEEE Transactions on Services Computing. Accepted