PREP：基于时间推移推理的流行度预测预训练
PREP: Pre-training with Temporal Elapse Inference for Popularity Prediction
摘要：
预测在线内容的流行度是各种应用中的基本问题。一个实际的挑战源于观测时间或预测范围的变化长度，即，期望用于流行度预测的良好模型来处理各种预测设置。然而，现有的方法大多采用单独的训练模式，难以将一种预测模式推广到其他预测模式，造成计算资源的极大浪费和对下游标签的大量需求。为了解决上述问题，我们提出了一种新的流行度预测预训练框架，即PREP，旨在从现成的未标记扩散数据中预训练通用表示模型，该模型可以有效地转移到各种预测设置中。我们设计了一个新的借口任务进行预训练，即，对流行度动态的两个随机采样的时间片进行时间流逝推断，促使表示模型学习关于流行度动态的内在知识。在两个真实数据集上进行的实验结果表明，对于不同的流行度预测任务设置，预训练框架的泛化和效率。
英文摘要：
Predicting the popularity of online content is a fundamental problem in various applications. One practical challenge takes roots in the varying length of observation time or prediction horizon, i.e., a good model for popularity prediction is desired to handle various prediction settings. However, most existing methods adopt a separate training paradigm for each prediction setting and the obtained model for one setting is difficult to be generalized to others, causing a great waste of computational resources and a large demand for downstream labels. To solve the above issues, we propose a novel pre-training framework for popularity prediction, namely PREP, aiming to pre-train a general representation model from the readily available unlabeled diffusion data, which can be effectively transferred into various prediction settings. We design a novel pretext task for pre-training, i.e., temporal elapse inference for two randomly sampled time slices of popularity dynamics, impelling the representation model to learn intrinsic knowledge about popularity dynamics. Experimental results conducted on two real datasets demonstrate the generalization and efficiency of the pre-training framework for different popularity prediction task settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
