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In this work, we identify open research opportunities in applying NeuralTemporal Point Process (NTPP) models to industry scale customer behavior databy carefully reproducing NTPP models published up to date on known literaturebenchmarks as well as applying NTPP models to a novel, real world consumerbehavior dataset that is twice as large as the largest publicly available NTPPbenchmark. We identify the following challenges. First, NTPP models, albeittheir generative nature, remain vulnerable to dataset imbalances and cannotforecast rare events. Second, NTPP models based on stochastic differentialequations, despite their theoretical appeal and leading performance onliterature benchmarks, do not scale easily to large industry-scale data. Theformer is in light of previously made observations on deep generative models.Additionally, to combat a cold-start problem, we explore a novel addition toNTPP models - a parametrization based on static user features.

Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data