Point-of-interest (POI) recommendation can help providing better user experience, and provide users with third-party information about restaurant or entertainment. There are several studies to predict the next POI where the user will go so as to recommend appropriate services. They use additional information such as text or location for more precise prediction, or manually define user patterns. However, it is costly to collect and analyze large amounts of data for POI recommendation. In this paper, we propose a novel method to recommend POI by extracting the personalized movement pattern only from the POI data without any additional information. We collected POI data ofl. 5M users for six months from smart card, and produce personalized POI and user embedding. Since it is hard to construct one POI recommendation model for 1.5 million people, we divide them to several groups according to their simple mobility pattern. Given a previous POI sequence, user and group id, the proposed model is trained to maximize the probability of the next POI. Although the learning method of the proposed model is simple, even if the given POI sequence is the same, successive POI can be predicted differently according to the user, resulting in personalized POI recommendation. The proposed model achieves 73.64%, 88.65%, and 91.54% in top-1, 3 and 5 accuracies which are higher than the performance of the baseline model (59.48%, 75.85%, and 80.1%, respectively). Besides, we verify the embedding performance of the proposed model through arithmetic operations between POI vectors.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Editors||Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|Publication status||Published - 2019 Dec|
|Event||2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States|
Duration: 2019 Dec 9 → 2019 Dec 12
|Name||Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019|
|Conference||2019 IEEE International Conference on Big Data, Big Data 2019|
|Period||19/12/9 → 19/12/12|
Bibliographical noteFunding Information:
This work was supported by grant funded by 2019 IT Promotion fund (Development of AI based Precision Medicine Emergency System) of the Korea government (Ministry of Science and ICT). J. Y. Kim has been supported by NRF (National Research Foundation of Korea) grant funded by the Korean government (NRF-2019-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management