A Spatial Insight for UGC Apps: Fast Similarity Search on Keyword-Induced Point Groups

Zhe Li, Yu Li, Man Lung Yiu
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引用次数: 2

Abstract

In the era of smartphones, massive data are generated with geo-related info. A large portion of them come from UGC applications (e.g., Twitter, Instagram), where the content provider are users themselves. Such applications are highly attractive for targeted marketing and recommendation, which have been well studied in recommendation system. In this paper, we consider this from a brand new spatial aspect using UGC contents only. To do this we first representing each message as a point with its geo info as its location and then grouping all the points by their keywords to form multiple point groups. We form a similarity search problem that given a query keyword, our problem aims to find k keywords with the most similar distribution of locations. Our case study shows that with similar distribution, the keywords are highly likely to have semantic connections. However, the performance of existing solutions degrades when different point groups have significant overlapping, which frequently happens in UGC contents. We propose efficient techniques to process similarity search on this kind of point groups. Experimental results on Twitter data demonstrate that our solution is faster than the state-of-the-art by up to 6 times.
UGC应用的空间洞察:关键字诱导点群的快速相似性搜索
在智能手机时代,与地理相关的信息产生了大量数据。其中很大一部分来自UGC应用(如Twitter、Instagram),这些应用的内容提供者就是用户本身。这些应用对于定向营销和推荐具有很强的吸引力,在推荐系统中已经得到了很好的研究。在本文中,我们只使用UGC内容从一个全新的空间角度来考虑这个问题。要做到这一点,我们首先将每个消息表示为一个点,其地理信息作为其位置,然后按其关键字分组所有点以形成多个点组。我们形成一个相似度搜索问题,给定一个查询关键字,我们的问题旨在找到k个位置分布最相似的关键字。我们的案例研究表明,在相似的分布下,关键字极有可能具有语义连接。然而,当不同的点组有明显的重叠时,现有解决方案的性能会下降,这种情况经常发生在UGC内容中。我们提出了一种有效的方法来处理这类点群的相似性搜索。Twitter数据的实验结果表明,我们的解决方案比最先进的解决方案快6倍。
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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