论文标题
烟：新闻推荐的细粒度和快速用户建模
FUM: Fine-grained and Fast User Modeling for News Recommendation
论文作者
论文摘要
用户建模对于新闻推荐很重要。现有方法通常首先将用户的单击新闻单独编码到新闻嵌入中，然后将其汇总到用户嵌入中。但是，这些方法忽略了来自同一用户的不同点击新闻的单词级交互，其中包含丰富的详细线索来推断用户兴趣。在本文中，我们提出了一个细粒度和快速的用户建模框架（FUM），以模拟从细粒度的行为互动中的用户兴趣以进行新闻推荐。 FUM的核心思想是将点击新闻连接到长文档中，并将用户建模转换为文档建模任务，并既有内在的新闻界和新闻中的单词级交互。由于Vanilla Transformer无法有效地处理长文档，因此我们将名为FastFormer的有效变压器应用于模型的细粒行为相互作用。在两个现实世界数据集上进行的大量实验证明了FUM可以有效，有效地对新闻建议的用户兴趣进行建模。
User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation.
