Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation
<|reference_start|>Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation: 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.<|reference_end|>
@article{ma2024modeling,
    title={Modeling User Intent Beyond Trigger: Incorporating Uncertainty for
  Trigger-Induced Recommendation},
    author={Jianxing Ma, Zhibo Xiao, Luwei Yang, Hansheng Xue, Xuanzhou Liu, Wen
  Jiang, Wei Ning, Guannan Zhang},
    journal={arXiv preprint arXiv:2408.03091},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03091},
    primaryClass={cs.IR}
}