Abstract
Sequence prediction is a well-defined problem with a proliferation of applications, such as recommendation systems, social media monitor, economic analysis, etc. Recently, RNN-based methodologies have shown their superiority in time-series data analysis and sequence modeling. The question of which event would happen next is not difficult to answer anymore, but the prediction of when it would happen is still a mountain to climb. In this paper, we propose a Multi-task model to predict both event and their continuous timestamps at the same time. Specifically, (1) we design a two-layer RNN encoder for event sequences and a CNN encoder for time sequences, both equipped with multi-head self-attention to align history features; (2) we form multiple generative adversarial models for predicting future time sequences to solve the problem of multi-modal time distribution; (3) Mixture learning losses are adopted to conduct a 3-step learning strategy for training our model, the cross-entropy loss for events, Huber loss and adversarial classification loss which induces the Wasserstein distance for times. Due to these characteristics, we name it MM-CPred. The experiments on 4 real-life datasets confirmed its improvements compared with the baselines.
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References
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017). arXiv:abs/1701.07875
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv preprint: arXiv:1406.1078
Fedus, W., Goodfellow, I., Dai, A.M.: MaskGAN: better text generation via filling in the blank. In: International Conference on Learning Representations (2018)
Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS (2014)
Granroth-Wilding, M., Clark, S.: What happens next? Event prediction using a compositional neural network model. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., Wang, J.: Long text generation via adversarial training with leaked information (2017). arXiv preprint: arXiv:abs/1709.08624
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huber, P.J.: Robust estimation of a location parameter. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics)Springer Series in Statistics (Perspectives in Statistics), pp. 492–518. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_35
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017)
LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks 3361(10), 1995 (1995)
Li, Y., Du, N., Bengio, S.: Time-dependent representation for neural event sequence prediction (2017). arXiv preprint arXiv:1708.00065
Li, Z., Ding, X., Liu, T.: Constructing narrative event evolutionary graph for script event prediction. In: IJCAI (2018)
Lin, K., Li, D., He, X., Zhang, Z., Sun, M.T.: Adversarial ranking for language generation. In: NIPS (2017)
Lin, L., Wen, L., Wang, J.: MM-Pred: a deep predictive model for multi-attribute event sequence. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 118–126. SIAM (2019)
Linden, G., Smith, B., Bullet, J.L.: Recommendations item-to-item collaborative filtering (2001)
Mei, H., Eisner, J.M.: The neural Hawkes process: a neurally self-modulating multivariate point process. In: Advances in Neural Information Processing Systems, pp. 6754–6764 (2017)
Musto, C., Semeraro, G., Degemmis, M., Lops, P.: Word embedding techniques for content-based recommender systems: an empirical evaluation. In: RecSys Posters (2015)
Neumann, L., Zisserman, A., Vedaldi, A.: Future event prediction: if and when. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Pichl, M., Zangerle, E., Specht, G.: Now playing on spotify: leveraging spotify information on twitter for artist recommendations. In: ICWE Workshops (2015)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW (2001)
Semeniuta, S., Severyn, A., Gelly, S.: On accurate evaluation of GANs for language generation (2018). ArXiv: arXiv:abs/1806.04936
Tavakol, M., Brefeld, U.: Factored MDPs for detecting topics of user sessions. In: RecSys (2014)
Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems, pp. 5998–6008 (2017)
Xu, J., Ren, X., Lin, J., Sun, X.: Diversity-promoting GAN: a cross-entropy based generative adversarial network for diversified text generation. In: EMNLP (2018)
Yi, X., Hong, L., Zhong, E., Liu, N.N., Rajan, S.: Beyond clicks: dwell time for personalization. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 113–120. ACM (2014)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient (2016). ArXiv:abs/1609.05473
Zhou, T., Qian, H., Shen, Z., Zhang, C., Wang, C., Liu, S., Ou, W.: Jump: a jointly predictor for user click and dwell time. In: IJCAI (2018)
Zhu, Y., et al.: What to do next: modeling user behaviors by time-LSTM. In: IJCAI, pp. 3602–3608 (2017)
Acknowledgment
The work was supported by the National Key Research and Development Program of China (No. 2019YFB1704003), the National Nature Science Foundation of China (No. 71690231), Tsinghua BNRist.
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Lin, L., Zong, Z., Wen, L., Qian, C., Li, S., Wang, J. (2021). MM-CPred: A Multi-task Predictive Model for Continuous-Time Event Sequences with Mixture Learning Losses. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12681. Springer, Cham. https://doi.org/10.1007/978-3-030-73194-6_34
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DOI: https://doi.org/10.1007/978-3-030-73194-6_34
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