In many classical e-commerce platforms, personalized recommendation has been
proven to be of great business value, which can improve user satisfaction and
increase the revenue of platforms. In this paper, we present a new
recommendation problem, Trigger-Induced Recommendation (TIR), where users'
instant interest can be explicitly induced with a trigger item and follow-up
related target items are recommended accordingly. TIR has become ubiquitous and
popular in e-commerce platforms. In this paper, we figure out that although
existing recommendation models are effective in traditional recommendation
scenarios by mining users' interests based on their massive historical
behaviors, they are struggling in discovering users' instant interests in the
TIR scenario due to the discrepancy between these scenarios, resulting in
inferior performance. To tackle the problem, we propose a novel recommendation
method named Deep Interest Highlight Network (DIHN) for Click-Through Rate
(CTR) prediction in TIR scenarios. It has three main components including 1)
User Intent Network (UIN), which responds to generate a precise probability
score to predict user's intent on the trigger item; 2) Fusion Embedding Module
(FEM), which adaptively fuses trigger item and target item embeddings based on
the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM),
which can effectively highlight users' instant interest from their behaviors
based on the result of FEM. Extensive offline and online evaluations on a
real-world e-commerce platform demonstrate the superiority of DIHN over
state-of-the-art methods.

本文提出一种新的推荐问题，即可以明确引起用户瞬时兴趣的触发项，并根据其推荐相关的库存项。通过在真实的电子商务平台上的离线和在线评估，本研究证明了 Deep Interest Highlight Network (DIHN) 在 Trigger-Induced Recommendation (TIR) 场景下胜过现有的方法。