# Forecasting Live Chat Intent from Browsing History

arXiv: https://arxiv.org/abs/2408.04668 · HTML: https://mlsanity.com/paper/2408.04668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://mlsanity.com/paper/2111.00607/) (2021)
- [Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation](https://mlsanity.com/paper/2006.06922/) (2020)
- [Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection](https://mlsanity.com/paper/2008.09982/) (2020)
- [Learning Multi-granularity User Intent Unit for Session-based Recommendation](https://mlsanity.com/paper/2112.13197/) (2021)
- [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://mlsanity.com/paper/2312.16199/) (2023)
- [Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks](https://mlsanity.com/paper/1807.08207/) (2018)
- [Predicting online user behaviour using deep learning algorithms](https://mlsanity.com/paper/1511.06247/) (2015)

## Built on by (most cited first, 1 of 1)

- [Bridging Conversational and Collaborative Signals for Conversational Recommendation](https://mlsanity.com/paper/2412.06949/) (2024)

## Abstract (arXiv)

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.