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Tag Recommendations In Social Bookmarking Systems

机译：社会书签系统中的标签建议

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摘要

Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurrences. We show that both FolkRank and collaborative filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
机译：协作标记系统允许用户向资源分配关键字（即所谓的“标签”）。标签用于导航，查找资源和偶然浏览，因此为用户带来直接好处。这些系统通常包括标签推荐机制，该机制简化了为资源找到好的标签的过程，但同时也巩固了跨用户的标签词汇。但是，实际上，仅应用了非常基本的推荐策略。在本文中，我们评估和比较了针对大型现实生活数据集的几种推荐算法：基于用户的协作过滤的改编，基于FolkRank算法构建的基于图的推荐器以及基于计数标签出现次数的简单方法。我们显示，与非个性化基准方法相比，FolkRank和协作过滤均提供了更好的结果。此外，由于基于计数标签出现的方法在计算上便宜，因此对于实时场景通常更可取，因此，我们讨论了改善此类方法性能的简单方法。我们展示了一个简单的基于推荐来自用户和资源的标签的推荐器，其性能几乎与最佳推荐器一样好。

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