Tag recommendations based on tensor dimensionality reduction
Tag recommendations based on tensor dimensionality reduction
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DOI：
10.1145/1454008.1454017
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
P. Symeonidis;A. Nanopoulos;Y. Manolopoulos
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文献类型：
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作者：
P. Symeonidis;A. Nanopoulos;Y. Manolopoulos
社交标签是许多用户以关键字形式添加元数据，注释和分类信息项（歌曲，图片，网络链接，产品等）的过程。协作标签系统根据其他用户用于同一项目的标签向用户推荐标签，旨在建立关于哪些标签最能描述项目的共识。但是，他们无法提供适当的标签建议，因为：（i）用户可能对信息项有不同的兴趣，并且（ii）信息项可能具有多个方面。与当前的标签建议算法相反，我们的方法开发了一个统一的框架，以建模社交标记系统中存在的三种实体：用户，项目和标签。这些数据由三阶张量表示，在该张量上，使用高阶奇异值分解（HOSVD）技术，对此进行了潜在的语义分析和尺寸降低。我们对所提出的方法进行了实验比较与两个最新的标签建议算法，并具有两个真实的数据集（last.fm和bibsonomy）。我们的结果表明，通过召回/精度衡量的有效性有了显着改善。
Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.