MFA-RNN: Uma Rede Neural Recorrente para Predição de Próximo Local de Visita com Base em Dados Esparsos
Resumo
Prever a mobilidade de um usuário é uma tarefa importante para se elevar a efetividade de aplicações móveis. Neste trabalho, é apresentada a MFA-RNN (Multi-Factor Attention Recurrent Neural Network), uma rede neural recorrente que utiliza a técnica Multi-Head Self-Attention para extrair correlações sob diversos aspectos da sequência de locais visitados. O modelo é capaz de prever o próximo local de visita considerando múltiplos fatores (usuário, localização, tempo e tipo do dia) de cada registro da sequência. Além disso, é proposto um método para o preenchimento de dados esparsos para melhorar o desempenho da solução. Os resultados obtidos indicam a eficácia do modelo MFA-RNN em relação a quatro soluções conhecidas na literatura.
Palavras-chave:
Predição de Próximo Local, Dados Esparsos, Rede Neural
Referências
Al-Molegi, A., Jabreel, M., and Ghaleb, B. (2016). Stf-rnn: Space time features-based recurrent neural network for predicting people next location. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7. IEEE.
Al-Molegi, A., Jabreel, M., and Martı́nez-Ballesté, A. (2018). Move, attend and predict: An attention-based neural model for people’s movement prediction. Pattern Recognition Letters, 112:34–40.
Al-Molegi, A. and Martı́nez-Ballesté, A. (2018). The effect of space-time representation learning in predicting people’s next location. In CCIA, pages 64–73.
Banovic, N., Buzali, T., Chevalier, F., Mankoff, J., and Dey, A. K. (2016). Modeling and understanding human routine behavior. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 248–260. ACM.
Capanema, C. G. S., Silva, F. A., and Silva, T. R. M. B. (2019). Identificação e classificação de pontos de interesse individuais com base em dados esparsos. Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos - SBRC 2019, pages 16–29.
Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., and Jin, D. (2018). Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference, pages 1459–1468. International World Wide Web Conferences Steering Committee.
Fu, R., Zhang, Z., and Li, L. (2016). Using lstm and gru neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pages 324–328. IEEE.
Gao, J., Sun, Y., Liu, W., and Yang, S. (2016). Predicting traffic congestions with global signatures discovered by frequent pattern mining. In 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pages 554–560. IEEE.
Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3):379–423.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems, pages 5998–6008.
Wei, L.-Y., Zheng, Y., and Peng, W.-C. (2012). Constructing popular routes from uncertain trajectories. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 195–203. ACM.
Yao, D., Zhang, C., Huang, J., and Bi, J. (2017). Serm: A recurrent model for next location prediction in semantic trajectories. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2411–2414. ACM.
Yao, Z., Fu, Y., Liu, B., Liu, Y., and Xiong, H. (2016). Poi recommendation: A temporal matching between poi popularity and user regularity. In 2016 IEEE 16th international conference on data mining (ICDM), pages 549–558. IEEE.
Zeng, J., He, X., Tang, H., and Wen, J. (2019). A next location predicting approach based on a recurrent neural network and self-attention. In International Conference on Collaborative Computing: Networking, Applications and Worksharing, pages 309– 322. Springer.
Al-Molegi, A., Jabreel, M., and Martı́nez-Ballesté, A. (2018). Move, attend and predict: An attention-based neural model for people’s movement prediction. Pattern Recognition Letters, 112:34–40.
Al-Molegi, A. and Martı́nez-Ballesté, A. (2018). The effect of space-time representation learning in predicting people’s next location. In CCIA, pages 64–73.
Banovic, N., Buzali, T., Chevalier, F., Mankoff, J., and Dey, A. K. (2016). Modeling and understanding human routine behavior. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 248–260. ACM.
Capanema, C. G. S., Silva, F. A., and Silva, T. R. M. B. (2019). Identificação e classificação de pontos de interesse individuais com base em dados esparsos. Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos - SBRC 2019, pages 16–29.
Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., and Jin, D. (2018). Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference, pages 1459–1468. International World Wide Web Conferences Steering Committee.
Fu, R., Zhang, Z., and Li, L. (2016). Using lstm and gru neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pages 324–328. IEEE.
Gao, J., Sun, Y., Liu, W., and Yang, S. (2016). Predicting traffic congestions with global signatures discovered by frequent pattern mining. In 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pages 554–560. IEEE.
Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543.
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3):379–423.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems, pages 5998–6008.
Wei, L.-Y., Zheng, Y., and Peng, W.-C. (2012). Constructing popular routes from uncertain trajectories. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 195–203. ACM.
Yao, D., Zhang, C., Huang, J., and Bi, J. (2017). Serm: A recurrent model for next location prediction in semantic trajectories. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2411–2414. ACM.
Yao, Z., Fu, Y., Liu, B., Liu, Y., and Xiong, H. (2016). Poi recommendation: A temporal matching between poi popularity and user regularity. In 2016 IEEE 16th international conference on data mining (ICDM), pages 549–558. IEEE.
Zeng, J., He, X., Tang, H., and Wen, J. (2019). A next location predicting approach based on a recurrent neural network and self-attention. In International Conference on Collaborative Computing: Networking, Applications and Worksharing, pages 309– 322. Springer.
Publicado
07/12/2020
Como Citar
CAPANEMA, Cláudio Gustavo Santos; SILVA, Fabrício Aguiar; SILVA, Thais Regina de Moura Braga.
MFA-RNN: Uma Rede Neural Recorrente para Predição de Próximo Local de Visita com Base em Dados Esparsos. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 127-140.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12278.
