Automatic acquisition of a taxonomy of microblogs users' interests
Automatic acquisition of a taxonomy of microblogs users' interests
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DOI：
10.1016/j.websem.2017.05.004
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发表时间：
2017-08-01
影响因子：
2.5
通讯作者：
Velardi, Paola
中科院分区：
文献类型：
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作者：
Faralli, Stefano;Stilo, Giovanni;Velardi, Paola
用户兴趣建模在当前的Web中扮演着重要的角色，因为它是许多服务（如推荐和定制）的基础。使用语义技术来表示用户的兴趣可以有助于减少诸如稀疏性、过度专业化和领域依赖性等问题，这些问题是已知的最先进的语义器的关键问题。在本文中，我们提出了一种高覆盖率建模的Twitter用户支持他们的兴趣，我们称之为Twixonomy的分层表示。为了自动构建群体、社区或单用户Twixonomy，我们首先在用户的友谊列表中识别“主题”朋友（即，代表兴趣而不是同龄人之间的社会关系的朋友）。我们将那些在维基百科上有相关页面的用户归类为主题用户。词义消歧算法用于为每个主题朋友选择合适的维基百科页面。接下来，从所考虑的Twitter人口的主要感兴趣的主题的wikipages的集合开始，我们提取连接这些页面与最顶端的维基百科类别节点的所有路径，然后我们有效地修剪所得到的图，以诱导直接的非循环图，并显着减少歧义，这是维基百科类别图的一个众所周知的问题。我们在创造性公共许可下发布本作品中产生的Twixonomy。(C)2017爱思唯尔B.V.保留所有权利。
Modeling users' interests plays an important role in the current web since it is at the basis of many services such as recommendation and customization. Using semantic technologies to represent users' interests may help to reduce problems such as sparsity, over-specialization and domain-dependency, which are known to be critical issues of state of the art recommenders. In this paper we present a method for high-coverage modeling of Twitter users supported by a hierarchical representation of their interests, which we call a Twixonomy. In order to automatically build a population, community, or single-user Twixonomy we first identify "topical'' friends in users' friendship lists (i.e., friends representing an interest rather than a social relation between peers). We classify as topical those users with an associated page on Wikipedia. A word-sense disambiguation algorithm is used to select the appropriate Wikipedia page for each topical friend. Next, starting from the set of wikipages representing the main topics of interests of the considered Twitter population, we extract all paths connecting these pages with topmost Wikipedia category nodes, and we then prune the resulting graph efficiently so as to induce a direct acyclic graph and significantly reduce over ambiguity, a well known problem of the Wikipedia category graph. We release the Twixonomy produced in this work under creative common license. (C) 2017 Elsevier B.V. All rights reserved.