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.

本文提出一种名为 HIFN 的层次兴趣融合网络，该网络通过包含短期兴趣提取器、长期兴趣提取器、兴趣融合模块和兴趣解缠模块这四个基本模块，有效地提取和融合用户的短期和长期兴趣，以提高个性化产品搜索中的点击率预测效果。