Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interests with respect to multiple aspects, how to extract and fuse users' long-term interest with short-term interests, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interests Extractor (SIE), Long-term Interests Extractor (LIE), Interests Fusion Module (IFM) and Interests Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module. In IFM, the achieved long and short-term interests are further fused in an adaptive manner, followed by concatenating it with original raw context features for the final prediction result. Last but not least, considering the entangling characteristic of long and short-term interests, IDM further devises a self-supervised framework to disentangle long and short-term interests. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of HIFN over state-of-the-art methods.

翻译：在个性化产品搜索中，点击率（CTR）估计是一项重要但具有挑战性的任务。然而，现有CTR方法在产品搜索场景中仍面临以下三个挑战：如何更有效地从多个方面提取用户短期兴趣，如何提取并融合用户长期兴趣与短期兴趣，以及如何解决长期与短期兴趣的纠缠特性。为解决这些问题，本文提出了一种名为分层兴趣融合网络（HIFN）的新方法，该方法包含四个基本模块：短期兴趣提取器（SIE）、长期兴趣提取器（LIE）、兴趣融合模块（IFM）和兴趣解耦模块（IDM）。具体而言，SIE通过集成三种基础兴趣编码器（即查询依赖、目标依赖和因果依赖兴趣编码器）来提取用户短期兴趣，并将生成的表示传递给LIE模块；LIE模块通过基于SIE模块短期兴趣设计注意力机制，有效捕获用户长期兴趣。在IFM中，所获得的长期和短期兴趣以自适应方式进一步融合，并与原始上下文特征拼接得到最终预测结果。最后，针对长期与短期兴趣的纠缠特性，IDM设计了自监督框架对其进行解耦。在真实电商平台上进行的离线与在线评估结果表明，HIFN相比现有最优方法具有显著优越性。