User-item interactions in social media provide a rich dataset for wide applications such as viral marketing and recommender systems. Post retweeting behaviors and venue check-in events by users are the most representative. While existing studies predict items’ rise and fall, i.e., tweet popularity and venue closure detection, using hand-crafted features, this paper aims at exploring feature representation learning to improve prediction performance. We target at two essential time-series classification tasks on social media, including Shutdown Risk Prediction (SRP) of venues and Tweet Popularity Prediction (TPP) of posts. We study how feature representation learning of items can benefit both SRP and TPP tasks. The main idea is to learn item embedding vectors as features in item-item graphs constructed from time series of check-in events and retweeting behaviors. The learned features are used together with manually-defined features to enlarge the representation capability. In the TPP task, we also propose a pattern-aware self-exciting point process (PSEISMIC) model to generate time-series features. Experiments conducted on Instagram, Foursquare, and Twitter datasets exhibit promising performance of jointly utilizing learned and extracted features in both tasks. PSEISMIC can also further boost TPP accuracy. The major contribution of this work is three-fold. First, we propose to jointly deal with SRP and TPP under the same framework of feature extraction and learning. Second, we show that feature presentation learning of items can benefit these two prediction tasks with time series data. Third, by incorporating time series patterns, the proposed PSEISMIC further improves the performance of popularity prediction.

社交媒体中的用户项目互动为病毒式营销和推荐系统等广泛应用提供了丰富的数据集。用户的转推行为和场所签到事件是最具代表性的。现有研究使用手工制作的功能预测物品的兴衰，即鸣叫声流行和场地封闭检测，但本文旨在探索特征表示学习以提高预测性能。我们针对社交媒体上的两个基本时间序列分类任务，包括场所的关机风险预测（SRP）和Tweet流行度预测（TPP）的帖子。我们研究了项目的特征表示学习如何使SRP和TPP任务受益。主要思想是学习项目嵌入向量，作为由签入事件和转推行为的时间序列构成的项目-项目图中的特征。学习的功能与手动定义的功能一起使用以扩大表示能力。在TPP任务中，我们还提出了一种模式感知的自激点过程（PSEISMIC）模型来生成时间序列特征。在Instagram，Foursquare和Twitter数据集上进行的实验展示了在两个任务中共同利用学习和提取的功能的有希望的性能。PSEISMIC还可以进一步提高TPP的准确性。这项工作的主要贡献是三方面的。首先，我们建议在相同的特征提取和学习框架下与SRP和TPP共同处理。其次，我们证明了项目的特征表示学习可以通过时间序列数据使这两个预测任务受益。第三，通过合并时间序列模式，提出的PSEISMIC进一步提高了流行度预测的性能。