利用无顺序标签关系进行上下文感知推荐
Leveraging Order-Free Tag Relations for Context-Aware Recommendation
摘要：
标签推荐依赖于顶级$k$标签的排名函数或自回归生成方法。然而，以前的方法忽略了标签集的两个看似矛盾但又令人满意的特性之一：无序性和相互依赖性。虽然排名方法未能解决标签之间的相互依赖性，当他们被排名，自回归方法未能考虑到无序，因为它被设计为利用令牌之间的顺序关系。我们提出了一种序列无关的标签推荐生成方法，其中下一个要生成的标签的顺序是独立的生成标签的顺序和地面真实标签发生在训练数据的顺序。在Instagram和Stack Overflow两个不同领域的实证结果表明，我们的方法明显优于以前的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
