Network-aware search in social tagging applications: instance optimality versus efficiency
Network-aware search in social tagging applications: instance optimality versus efficiency
复制标题
社交标签应用中的网络感知搜索：实例最优性与效率
DOI：
10.1145/2505515.2505760
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Bogdan Cautis
中科院分区：
文献类型：
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作者：
Silviu Maniu;Bogdan Cautis
在本文中，我们考虑在社会应用程序中的top-k查询回答，重点是社会标记。这个问题需要从社会不可知论的技术显著偏离。在网络感知的上下文中，可以（并且应该）利用社交链接，其可以指示用户如何与搜索者相关以及他们的标记动作在结果构建中应该具有多少权重。我们提出的算法，有可能扩展到当前的应用程序。虽然这个问题已经在以前的文献中被考虑过，但这是在强简化假设下完成的，或者是在不能扩展到甚至中等规模的现实世界应用程序的选择下完成的。我们首先重新审视问题的一个关键方面，即访问给定搜索者的最接近或最相关的用户。我们描述了如何在飞行中（没有任何预先计算）完成几个可能的选择-可以说是最自然的-在用户网络中的邻近计算。在此基础上，我们的top-k算法是健全和完整的，解决了现有的适用性问题。此外，它的性能总体上要好得多，并且在搜索完全依赖于标记动作的社交权重的情况下是实例最佳的。为了进一步解决在线应用程序的效率需求，精确的搜索，虽然是最佳的，可能仍然是昂贵的，然后我们考虑近似算法。具体来说，这些依赖于关于社交网络的简明统计数据或近似的最短路径计算。对Twitter真实数据的广泛实验表明，我们的技术可以大大提高响应时间，而不会牺牲精度。
We consider in this paper top-k query answering in social applications, with a focus on social tagging. This problem requires a significant departure from socially agnostic techniques. In a network- aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose algorithms that have the potential to scale to current applications. While the problem has already been considered in previous literature, this was done either under strong simplifying assumptions or under choices that cannot scale to even moderate-size real-world applications. We first revisit a key aspect of the problem, which is accessing the closest or most relevant users for a given seeker. We describe how this can be done on the fly (without any pre- computations) for several possible choices -- arguably the most natural ones -- of proximity computation in a user network. Based on this, our top-k algorithm is sound and complete, addressing the applicability issues of the existing ones. Moreover, it performs significantly better in general and is instance optimal in the case when the search relies exclusively on the social weight of tagging actions. To further address the efficiency needs of online applications, for which the exact search, albeit optimal, may still be expensive, we then consider approximate algorithms. Specifically, these rely on concise statistics about the social network or on approximate shortest-paths computations. Extensive experiments on real-world data from Twitter show that our techniques can drastically improve response time, without sacrificing precision.
DOI：
10.1145/1242572.1242640
发表时间：
2007-05
期刊：
--
影响因子：
--
作者：
Shenghua Bao;Gui-Rong Xue;Xiaoyuan Wu;Yong Yu;Ben Fei;Zhong Su
通讯作者：
Shenghua Bao;Gui-Rong Xue;Xiaoyuan Wu;Yong Yu;Ben Fei;Zhong Su