Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.

翻译：历史行为在包括推荐和信息检索在内的多种预测任务中展现了显著效果和潜力。整体历史行为多样但嘈杂，而搜索行为往往稀疏。现有的个性化搜索排序方法大多采用稀疏搜索行为进行学习，面临瓶颈，未能充分挖掘关键的长程兴趣。事实上，用户长程兴趣对即时搜索而言无疑多样且嘈杂，如何有效利用它仍是一个开放问题。为解决此问题，本文提出一种名为查询主导的用户兴趣网络（QIN）的新模型，包含两个级联单元，用于过滤原始用户行为并重新加权行为子序列。具体地，我们提出相关性搜索单元（RSU），旨在首先搜索与查询相关的子序列，再搜索与目标物品相关的子子序列。这些物品随后输入到名为融合注意力单元（FAU）的注意力单元中，该单元能够分别从ID字段和属性字段计算注意力分数，并基于过去时段用户参与度自适应地融合物品嵌入和内容嵌入。在真实数据集上的大量实验和消融研究证明了我们的模型优于现有最先进方法。QIN现已成功部署于快手搜索（一个在线视频搜索平台），点击率提升7.6%。