  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.
