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.
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