Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience
<|reference_start|>Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience: Explanations are crucial for enhancing user trust and understanding within modern recommendation systems. To build truly explainable systems, we need high-quality datasets that elucidate why users make choices. While previous efforts have focused on extracting users' post-purchase sentiment in reviews, they ignore the reasons behind the decision to buy.   In our work, we propose a novel purchase reason explanation task. To this end, we introduce an LLM-based approach to generate a dataset that consists of textual explanations of why real users make certain purchase decisions. We induce LLMs to explicitly distinguish between the reasons behind purchasing a product and the experience after the purchase in a user review. An automated, LLM-driven evaluation, as well as a small scale human evaluation, confirms the effectiveness of our approach to obtaining high-quality, personalized explanations. We benchmark this dataset on two personalized explanation generation tasks. We release the code and prompts to spur further research.<|reference_end|>
@article{chen2024unlocking,
    title={Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark
  for Purchase Reason and Post-Purchase Experience},
    author={Tao Chen, Siqi Zuo, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael
  Bendersky},
    journal={arXiv preprint arXiv:2402.13417},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13417},
    primaryClass={cs.IR}
}