Forecasting Live Chat Intent from Browsing History
<|reference_start|>Forecasting Live Chat Intent from Browsing History: Customers reach out to online live chat agents with various intents, such as asking about product details or requesting a return. In this paper, we propose the problem of predicting user intent from browsing history and address it through a two-stage approach. The first stage classifies a user's browsing history into high-level intent categories. Here, we represent each browsing history as a text sequence of page attributes and use the ground-truth class labels to fine-tune pretrained Transformers. The second stage provides a large language model (LLM) with the browsing history and predicted intent class to generate fine-grained intents. For automatic evaluation, we use a separate LLM to judge the similarity between generated and ground-truth intents, which closely aligns with human judgments. Our two-stage approach yields significant performance gains compared to generating intents without the classification stage.<|reference_end|>
@article{yoon2024forecasting,
    title={Forecasting Live Chat Intent from Browsing History},
    author={Se-eun Yoon, Ahmad Bin Rabiah, Zaid Alibadi, Surya Kallumadi, and
  Julian McAuley},
    journal={arXiv preprint arXiv:2408.04668},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04668},
    primaryClass={cs.CL cs.AI cs.IR}
}