A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
Next (or successive) point-of-interest (POI) recommendation has attracted increasing attention in recent years. Most of the previous studies attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user’s next move. However, none of these approaches utilized the social influence of each user’s friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. Also, we carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is of high efficiency among six neural-network- and attention-based methods.
Next, we introduce how to run our model for provided example data or your own data.
Python 3.7
TensorFlow 1.2.0
Numpy 1.15.0
As an illustration, we provide the data and running command for Gowalla and Brightkite.
userlocation.csv：includes user ID, POI ID, latitude, longitude, checkin time.
locations.csv: includes POI ID, latitude, longitude, city ID
location_embedding.csv: includes the embeddings of all locations of all POIs
user_embedding.csv: includes the embeddings of all users
network.csv: includes all friendships of all users (from usrID, to userID)
Liwei Huang, dr_huanglw@163.com
This work has been published on ACM transactions on Internet technology. If you use DAN-SNR in your research, please cite our paper:
Liwei Huang, Yutao Ma, Yanbo Liu, and Keqing He. 2020. DAN-SNR: A Deep Attentive Network for Social aware Next Point-of-interest Recommendation. ACM Trans. Internet Technol. 21, 1, Article 2 (December 2020), 27 pages. https://doi.org/10.1145/3430504