DLP-KDD 2021

3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2021

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Reference

Here we provide a list of relevant workshop and papers.

Website for dlp-kdd2020 can be found here

Website for dlp-kdd2019 can be found here

Other similar workshops:

Bibliography

  • [1]. Park, Jongsoo, et al. “Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications.” arXiv preprint arXiv:1811.09886 (2018).
  • [2]. Grbovic, Mihajlo, and Haibin Cheng. “Real-time personalization using embeddings for search ranking at Airbnb.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018.
  • [3]. Haldar, Malay, et al. “Applying Deep Learning To Airbnb Search.” arXiv preprint arXiv:1810.09591 (2018).
  • [4]. Sculley, David, et al. “Hidden technical debt in machine learning systems.” Advances in neural information processing systems. 2015.
  • [5]. Baylor, Denis, et al. “Tfx: A tensorflow-based production-scale machine learning platform.” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.
  • [6]. Crankshaw, Daniel, et al. “Clipper: A Low-Latency Online Prediction Serving System.” NSDI. 2017.
  • [7]. Zhou, Guorui, et al. “Deep Interest Evolution Network for Click-Through Rate Prediction.” Proceedings of the 33nd AAAI Conference on Artificial Intelligence, 2019.
  • [8]. Ge, Tiezheng, et al. “Image matters: Visually modeling user behaviors using advanced model server.” Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018.
  • [9]. Zhu, Han, et al. “Learning Tree-based Deep Model for Recommender Systems.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018.
  • [10]. Zhou, Guorui, et al. “Deep interest network for click-through rate prediction.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018.
  • [11]. Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. SIGIR (2018).
  • [12]. Mu, Ruihui. “A Survey of Recommender Systems Based on Deep Learning.” IEEE Access 6 (2018): 69009-69022.
  • [13]. Zheng, Lei, et al. “MARS: Memory Attention-Aware Recommender System.” arXiv preprint arXiv:1805.07037 (2018).
  • [14]. Zhou, Guorui, et al. “Rocket launching: A universal and efficient framework for training well-performing light net.” Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
  • [15]. Ai, Qingyao, et al. “Learning groupwise scoring functions using deep neural networks.” In WSDM’19 Workshop on Deep Matching in Practical Applications (DAPA 19).
  • [16]. Bonawitz, Keith, et al. “Towards Federated Learning at Scale: System Design.” arXiv preprint arXiv:1902.01046 (2019).
  • [17]. Naumov, Maxim. “On the Dimensionality of Embeddings for Sparse Features and Data.” arXiv preprint arXiv:1901.02103 (2019).
  • [18]. Frolov, Evgeny, and Ivan Oseledets. “Tensor methods and recommender systems.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7.3 (2017): e1201.