Personalized recommendation in social tagging systems using hierarchical clustering
Personalized recommendation in social tagging systems using hierarchical clustering
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DOI：
10.1145/1454008.1454048
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Andriy Shepitsen;Jonathan F. Gemmell;B. Mobasher;R. Burke
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文献类型：
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作者：
Andriy Shepitsen;Jonathan F. Gemmell;B. Mobasher;R. Burke
协作式标签应用程序允许互联网用户使用个性化标签对资源进行注释。由许多注释创建的复杂网络通常被称为大众分类法，允许用户自由地探索标签、资源甚至其他用户的个人资料，而不受严格的预定义概念层次结构的约束。然而，给用户提供的自由是有代价的：不受控制的词汇表可能会导致标签冗余和歧义，阻碍导航。数据挖掘技术，如集群，提供了一种通过识别趋势和减少噪音来补救这些问题的方法。标签簇也可以作为有效的个性化推荐的基础，辅助用户导航。提出了一种基于层次标签聚类的个性化推荐算法。我们的基本推荐框架独立于聚类方法，但我们使用了一种上下文相关的分层聚集聚类的变体，在聚类选择时考虑了用户的当前导航上下文。我们给出了在两个真实世界数据集上的广泛实验结果。虽然个性化算法在这两种情况下都是成功的，但我们的结果表明，只包含一个主题域的大众分类法，而不是许多主题，提供了一个更容易的推荐目标，可能是因为它们更有针对性，而且通常不那么稀疏。此外，作为个性化算法中不可或缺的一步，上下文相关的聚类选择在多主题分类中比在单主题分类中显示出更好的推荐效果。这一观察结果表明，在多主题大众分类中，主题选择是一种重要的推荐策略。
Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.