We Didn't Miss You: Interpolating Missing Opinions

Iuliia Chepurna, M. Makrehchi
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Abstract

When mining user streams from social media, activity gaps are inevitable, which is known as the sparsity of user data. Such sparsity can significantly degrade the performance of a predictive system that relies on time-sensitive user content. To mitigate this issue, conventional approaches generally tend to discard periods with missing data. However, this solution leads to neglecting information generated by other users which, if utilized, could potentially enhance the quality of the predictive model. So the following question arises: is it possible to alleviate the impact of absent data while preserving the available content contributed within the same timespan? Despite the fact that this problem is well-known, it has not been thoroughly studied before. The goal of this work is to find a way of interpolating missing data from user's network and his previous activities. We investigate how different types of user profiles affect overall behavior predictability. Proposed models are evaluated on a case study of a micro-blogging system for the investment community.
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我们没有错过你:插入缺失的意见
从社交媒体中挖掘用户流时,不可避免地会出现活动缺口,这就是用户数据的稀疏性。这种稀疏性会显著降低依赖于对时间敏感的用户内容的预测系统的性能。为了缓解这个问题,传统的方法通常倾向于丢弃丢失数据的周期。然而,这种解决方案会导致忽略其他用户生成的信息,如果利用这些信息,可能会潜在地提高预测模型的质量。因此,出现了以下问题:是否有可能减轻缺失数据的影响,同时保留相同时间范围内提供的可用内容?尽管这个问题众所周知,但以前还没有对它进行过深入的研究。这项工作的目标是找到一种从用户网络和他以前的活动中插入缺失数据的方法。我们研究了不同类型的用户配置文件如何影响整体行为的可预测性。本文以投资社区的微博系统为例,对所提出的模型进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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