DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction

Chen，Quanjun1,2; Jiang，Renhe2; Yang，Chuang2; Cai，Zekun3; Fan，Zipei1,2; Tsubouchi，Kota3; Shibasaki，Ryosuke1,2; Song，Xuan1,2

10.1145/3397536.3422221

2020-11-03

GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

283-292

Nowadays, GPS devices have increased explosively and produced huge amounts of trajectory data related to people's outgoing. Through those big location data, many researches aim to analyze human mobility for urban development, such as human movement prediction/modeling, POI (Point-Of-Interest) recommendation. However, trajectory data only contains timestamp and location information. The intention of human movement is not explicit so that it is hard to understand why people go to somewhere. The intention prior to the activity could be of great significance for analyzing and predicting human mobility, which has not been taken into consideration by the existing researches until the present. Thus, in this study, we propose a brand-new concept called human intentional mobility, aiming to employ intention information to predict people's outgoing. We carefully utilize user's search query to sense his intention as well as the intensity. For instance, if a user searches a certain POI for many times in a short period, it will represent a relatively high intention to go there. Then, to fully utilize this intention representation for predicting whether user will visit searched POI or not, we specially design Dual Sequential Interaction Network (DualSIN) as a novel and unique deep-learning model, which can effectively capture the sophisticated interactions among two kinds of sequential information (i.e., search sequence and mobility sequence) and typical categorical information (i.e., user attributes). Last, we evaluate our model on real-world dataset collected from Yahoo! Japan portal application, and demonstrate that it can achieve superior satisfactory performances to the-state-of-the-art models on multiple POI search queries.

deep learning recommender system ubiquitous and mobile computing user behavior modeling

第一 ; 通讯

英语

EI

20205009603896

Forecasting ; Urban growth ; Behavioral research ; User profile ; Deep learning ; Motion estimation ; Learning systems

Urban Planning and Development:403.1 ; Ergonomics and Human Factors Engineering:461.4 ; Computer Applications:723.5 ; Social Sciences:971

2-s2.0-85097262922

Scopus

1.SUSTech-UTokyo Joint Research Center on Super Smart City,Department of Computer Science and Engineering,Southern University of Science and Technology,China
2.Center for Spatial Information Science,University of Tokyo,Japan
3.Yahoo Japan Corporation,Japan

Chen，Quanjun,Jiang，Renhe,Yang，Chuang,et al. DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction[C],2020:283-292.