本論文研究活動意圖類型預測方法，以遞迴類神經網路架構為基礎，用建立群組模型的概念，比較四種建立模型的架構。第一種是全體資料模型，以所有GPS軌跡資料擷取特徵作為模型輸入，建立全體資料模型。第二種是群組模型，本論文提出兩種分群方法，分別為使用者為單位進行分群的使用者分群法，及以序列為單位進行分群的序列分群法，再以群組資料建立群組模型。第三種是遷移學習模型，以全體資料進行訓練，將全體資料訓練好的參數設置為初始參數，以群組資料作為訓練資料，只對模型中部份層的參數進行調整。第四種是合成模型，將全體資料模型和群組模型預測結果，學習調和參數將兩個預測結果進行比重相加。實驗評估顯示，遷移學習模型在OSM資料集的預測結果優於全體資料模型和群組模型，合成模型在大部分情況下可良好地結合兩模型，正確預測出使用者的活動意圖。在Geolife資料集中，合成模型在Accracy@5最高可達89.31%的準確率，在OSM資料集中，則可達74.12%的準確率。

In this paper, we study the problem of predicting activity intentions based on the recurrent neural network architecture. We constructed four learning models based on the various combinations of training data sets and recurrent neural network architectures. The first one is the global model, which uses all activity sequences as the training data set. The second one is the group model, in which two clustering methods: user-based clustering and sequence-based clustering methods are proposed to separate the data into groups. Accordingly, a prediction model is constructed respectively for each group of training data. The third one is the transfer-learning model, in which the parameters learned from all training data set are set as the initial parameters. Then the training data in each group is used to adjust the parameters from the middle layer of the RNN architecture to construct the predicting model for each group. The last one is the ensemble model, which concatenates the predicting results of the global model and the group model to learn the ensemble parameters to get a properly weighted sum of the two predicted results. The results of experiments show that the transfer-learning model on the OSM dataset has better performance than the global model and the group model. Furthermore, the ensemble model can combine the results of two models well in most cases and provide the highest accuracy. In the Geolife dataset, the accuracy@5 of the ensemble model achieves 89.31%, and gets 74.12% on the OSM dataset.

[1] J. R. Benetka, K. Balog and K.Norvag ,“Anticipating Information Needs Based on Check-in Activity,” in Proceedings of the 10th ACM International Conference on Web Search and Data Mining(WSDM), 2017.
[2] J. Bao, Y. Zheng and M. F. Mokbel ,“Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Network Data,” in Proceedings of the 20th International Conference on Advances in Geographic Information Systems(SIGSPATIAL), 2012.
[3] X. Chen, D. Shi, B. Zhao and F. Liu ,“Periodic Pattern Mining Based on GPS Trajectories,” in Proceedings International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE), 2016.
[4] S. Ghosh and S. K. Ghosh ,“Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users , “in Proceedings of the 26th ACM International Conference on World Wide Web Companion(WWW), 2017.
[5] S. Ghosh and S. K. Ghosh ,“THUMP: Semantic Analysis on Trajectory Traces to Explore Human Movement Patterns,” in Proceedings of the 25th International Conference Companion on World Wide Web(WWW), 2016.
[6] Y. Kim, J. Han and C. Yuan ,“TOPTRAC: Topical Trajectory Pattern Mining,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD), 2015.
[7] M. Lv, L. Chen, and G. Chen ,“Discovering Personally Semantic Places from GPS Trajectories,” In Proceedings of the 21st ACM international conference on Information and knowledge management(CIKM), 2012.
[8] A.Likhyani, D. Padmanabhan, S. Bedathur, and S. Mehta ,“Inferring and Exploiting Categories for Next Location Prediction,” in Proceedings of the 24th International Conference on World Wide Web(WWW), 2015.
[9] J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma ,“Neural Attentive Session-based Recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management(CIKM), 2017.
[10] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu and W. Y. Ma ,“Mining User Similarity Based on Location History, ” in Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems(GIS), 2008.
[11] J. McInerney, A. Rogers, and N. R. Jennings ,“Improving Location Prediction Services for New Users with Probabilistic Latent Semantic Analysis,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing(UbiComp), 2012.
[12] A. Noulas, S. Scellato, N. Lathia, and C. Mascolo ,“Mining User Mobility Features for Next Place Prediction in Location-based Services,” in Proceedings of the 2012 IEEE 12th International Conference on Data Mining(ICDM), 2012.
[13] E. Palumbo, G. Rizzo, R. Troncy, and E. Baralis ,“Predicting Your Next Stop-over from Location-based Social Network Data with Recurrent Neural Networks,” in RecTour, 2017.
[14] F. Wu and Z. Li ,“Where Did You Go: Personalized Annotation of Mobility Records,” in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management(CIKM), 2016.
[15] X. Xiao, Y. Zheng, Q. Luo, and X. Xie ,“Finding Similar Users using Category-Based Location History,” in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS), 2010.
[16] J. Ye, Z. Zhu, and H. Cheng ,“What’s Your Next Move: User Activity Prediction in Location-based Social Networks,” in Proceedings of the 2013 SIAM International Conference on Data Mining(SIAM), 2013.
[17] M. Zhou, Z. Ding, J. Tang, and D. Yin ,“Micro Behaviors: A New Perspective in E-commerce Recommender Systems,” in Proceedings of the 16th ACM International Conference on Web Search and Data Mining(WSDM), 2018
[18] Y. Zheng, X. Xie and W. Y. Ma ,“GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory,” in IEEE 2008.
[19] V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang ,“Collaborative Location and Activity Recommendations with GPS History Data,” in Proceedings of the 18th international conference on World wide web(WWW) 2010.
[20] Y. Zheng, L. Zhang and X. Xie, W. Y. Ma ,“Mining Correlation between Location Using Human Location History,” in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(GIS), 2009