[2D1-OS-6] 移動系列のデータマイニングと機械学習(1/1)

[2D1-OS-6-03] Accuracy Improvement of User Move Embedding with Latitude and Longitude Information

〇Takeshi Saga^1,2, Hiroki Tanaka^1,2, Satoshi Nakamura^1,2 (1. RIKEN Center for Advanced Intelligence Project Tourism Information Analytics Team, 2. Nara Institute of Science and Technology)

Keywords:Behavior analysis, Time series data, Distributed representation, Bidirectional LSTM, Hierarchical clustering

Recently, with the rise of the popularity of wearable devices like smartphones, it is becoming possible to use user’s moving records as a part of big data. In previous research, user movements were modeled by Bi-directional LSTM trained with time series of Mesh-ID. However, since Mesh-ID was assigned artificially, its physical distance and relative position were not considered properly. Therefore, it might be difficult to train the model effectively by using only Mesh-ID. In this research, to solve this problem, we used additional latitude and longitude information in the new model. As a result, we confirmed the accuracy improvement for Mesh-ID prediction and the difference between the output of clustering with user embeddings for each model.