カンファレンス (国際) DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction
Quanjun Chen (The University of Tokyo), Renhe Jiang (The University of Tokyo), Chuang Yang (The University of Tokyo), Zekun Cai (The University of Tokyo), Zipei Fan (The University of Tokyo), Kota Tsubouchi, Xuan Song (The University of Tokyo), Ryosuke Shibasaki (The University of Tokyo)
28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020)
Nowadays, GPS devices has increased explosively and produce 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 predic- tion/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 predict- ing 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 out- going. We carefully utilize user’s search query to sense the user’s 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 inten- tion representation for predicting whether user will visit searched POI or not, we specially design Dual Sequential Interaction Net- work (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.