论文标题
将神经时间点过程应用于大规模行业数据的挑战和机会
Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data
论文作者
论文摘要
在这项工作中，我们通过仔细复制最新发布的NTPP模型，在已知的文献基准上发布最新的NTPP模型，并将NTPP模型应用于新颖的，现实世界的消费者行为数据集，该模型是最大的，是最大的，是最大的，是最大的，是最大的NTPP公共可用的NTPP公共可用的NTPP Benchmark，并将NTPP模型应用于最新的NTPP模型，从而确定了开放的研究机会，以应用神经暂时点过程（NTPP）模型来扩展客户行为数据。我们确定以下挑战。首先，NTPP模型尽管其生成性质仍然容易受到数据集失衡的影响，并且无法预测罕见事件。其次，尽管具有理论上的吸引力和对文献基准的领先表现，但基于随机微分方程的NTPP模型并不能轻易地扩展到大型行业规模数据。鉴于先前对深层生成模型的观察，前者是。此外，为了解决一个冷门问题，我们探索了NTPP模型的新颖补充 - 基于静态用户功能的参数化。
In this work, we identify open research opportunities in applying Neural Temporal Point Process (NTPP) models to industry scale customer behavior data by carefully reproducing NTPP models published up to date on known literature benchmarks as well as applying NTPP models to a novel, real world consumer behavior dataset that is twice as large as the largest publicly available NTPP benchmark. We identify the following challenges. First, NTPP models, albeit their generative nature, remain vulnerable to dataset imbalances and cannot forecast rare events. Second, NTPP models based on stochastic differential equations, despite their theoretical appeal and leading performance on literature benchmarks, do not scale easily to large industry-scale data. The former is in light of previously made observations on deep generative models. Additionally, to combat a cold-start problem, we explore a novel addition to NTPP models - a parametrization based on static user features.
