  To cater to users' desire for an immersive browsing experience, numerous
e-commerce platforms provide various recommendation scenarios, with a focus on
Trigger-Induced Recommendation (TIR) tasks. However, the majority of current
TIR methods heavily rely on the trigger item to understand user intent, lacking
a higher-level exploration and exploitation of user intent (e.g., popular items
and complementary items), which may result in an overly convergent
understanding of users' short-term intent and can be detrimental to users'
long-term purchasing experiences. Moreover, users' short-term intent shows
uncertainty and is affected by various factors such as browsing context and
historical behaviors, which poses challenges to user intent modeling. To
address these challenges, we propose a novel model called Deep Uncertainty
Intent Network (DUIN), comprising three essential modules: i) Explicit Intent
Exploit Module extracting explicit user intent using the contrastive learning
paradigm; ii) Latent Intent Explore Module exploring latent user intent by
leveraging the multi-view relationships between items; iii) Intent Uncertainty
Measurement Module offering a distributional estimation and capturing the
uncertainty associated with user intent. Experiments on three real-world
datasets demonstrate the superior performance of DUIN compared to existing
baselines. Notably, DUIN has been deployed across all TIR scenarios in our
e-commerce platform, with online A/B testing results conclusively validating
its superiority.
