A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: 1) Tag cloud generation discovers potentially relevant tags in an application domain; 2) Tag expansion finds a sufficient set of tags upon original tags; and 3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments.

标签感知推荐系统（TRS）在用户配置文件中提出了标签稀疏性的挑战。先前的工作着重于扩展相似的标签，并且不将标签​​与相应的资源链接在一起，因此导致推荐中的静态用户配置文件。在本文中，我们提出了一种新的社交标签扩展模型（STEM），以生成动态用户配置文件以改善推荐性能。新模型不是简单地包含最相关的标签，而是通过利用标签之间的关系扩展标签来关注用户配置文件的完整性，并包括一组足够的标签来缓解标签稀疏性问题。基于STEM的新型TRS包含三个操作：1）标签云生成发现应用程序域中潜在的相关标签；2）标签扩展可以在原始标签上找到足够的标签集；3）用户配置文件重构可构建动态用户配置文件，并确定配置文件中扩展标签的权重。我们根据推荐准确性分析了STEM属性，并通过在多个数据集上进行的广泛实验证明了其性能。分析和实验结果表明，新的STEM技术能够正确地找到足够的标签集，并通过解决标签稀疏性问题来提高推荐准确性。在这一点上，这项技术在这些广泛的实验中始终优于最新的标签感知推荐方法。