论文标题
利用无订单的标签关系进行上下文感知建议
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
论文作者
论文摘要
TAG建议依赖于顶部$ K $标签的排名功能或自动回归生成方法。但是，以前的方法忽略了标签集的两个看似相互冲突但理想的特征之一：无序性和相互依赖性。虽然排名方法在排名时无法解决标签之间的相互依赖性，但自回归方法未能考虑到无序，因为它旨在利用令牌之间的顺序关系。我们为标签建议提出了一种符合序列的生成方法，其中要生成的下一个标签与生成标签的顺序和训练数据中发生的地面真相标签的顺序无关。在Instagram和堆栈溢出的两个不同领域上的经验结果表明，我们的方法比以前的方法要好得多。
Tag recommendation relies on either a ranking function for top-$k$ tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.
