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Prompt-augmented Temporal Point Process for Streaming Event Sequence

Siqiao Xue · Yan Wang · Zhixuan Chu · Xiaoming Shi · Caigao JIANG · Hongyan Hao · Gangwei Jiang · Xiaoyun Feng · James Zhang · Jun Zhou

Great Hall & Hall B1+B2 (level 1) #813
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Wed 13 Dec 8:45 a.m. PST — 10:45 a.m. PST


Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real world applications, the event data typically comes in a streaming manner, where the distribution of the patterns may shift over time. Under the privacy and memory constraints commonly seen in real scenarios, how to continuously monitor a TPP to learn the streaming event sequence is an important yet under-investigated problem. In this work, we approach this problem by adopting Continual Learning (CL), which aims to enable a model to continuously learn a sequence of tasks without catastrophic forgetting. While CL for event sequence is less well studied, we present a simple yet effective framework, PromptTPP, by integrating the base TPP with a continuous-time retrieval prompt pool. In our proposed framework, prompts are small learnable parameters, maintained in a memory space and jointly optimized with the base TPP so that the model is properly instructed to learn event streams arriving sequentially without buffering past examples or task-specific attributes. We formalize a novel and realistic experimental setup for modeling event streams, where PromptTPP consistently sets state-of-the-art performance across two real user behavior datasets.

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