Incorporating Relevance and Importance for Dynamic Ranking in Folksonomies
Incorporating Relevance and Importance for Dynamic Ranking in Folksonomies
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DOI：
10.4156/jcit.vol5.issue8.11
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
Kaipeng Liu;Binxing Fang;Weizhe Zhang
中科院分区：
文献类型：
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作者：
Kaipeng Liu;Binxing Fang;Weizhe Zhang
随着社会化标签系统的迅速普及以及用户和资源数量的不断增长，在大众分类法中寻找专家用户和相关资源变得越来越困难。在本文中，我们提出了一个基于二分图的动态排名算法，RicoRank（相关性和重要性在CORporated RANK），以提高搜索性能的大众分类。我们联合收割机的查询相关性和重要性有效地产生最终的排名得分。我们从一个平滑的概率生成模型，证明用户的兴趣和资源内容的查询相关性。我们用用户和资源之间的相互强化来描述其重要性。我们为每个相互增强的关系分配一个权重，该权重对应于关联标签、用户兴趣和资源内容之间的一致性。最后，我们采用了一个迭代的过程，它结合了查询的相关性和重要性，同时计算用户和资源的排名得分。我们在从真实世界系统收集的数据集上进行实验。在用户专业知识和资源质量排名上的实验结果表明，该算法具有令人信服的性能。
The rapidly increasing popularity of social tagging systems and growing amount of users and resources make it a difficult task to find expert users and relevant resources in folksonomies. In this paper, we propose a bipartite graph-based dynamic ranking algorithm, RicoRank (Relevance and Importance inCOrporated RANK), for improving search performance in folksonomies. We combine both the query relevance and importance effectively to generate the final ranking score. We derive the query relevance from a smoothed probabilistic generative model that demonstrates user interest and resource content. We characterize the importance with the mutual reinforcement between users and resources. We assign each mutual reinforcing relation with a weight corresponding to the coherence between the associated tags, the user interest and the resource content. Finally, we employ an iterative procedure, which incorporates well with both query relevance and importance, to simultaneously compute the ranking scores of users and resource. We conduct experiments on a dataset collected from a real-world system. Experimental results on both user expertise and resource quality ranking show a convincing performance of the proposed algorithm.