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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13417 |
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| _version_ | 1866912120622284800 |
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| author | Chen, Tao Zuo, Siqi Li, Cheng Zhang, Mingyang Mei, Qiaozhu Bendersky, Michael |
| author_facet | Chen, Tao Zuo, Siqi Li, Cheng Zhang, Mingyang Mei, Qiaozhu Bendersky, Michael |
| contents | In business and marketing, analyzing the reasons behind buying is a fundamental step towards understanding consumer behaviors, shaping business strategies, and predicting market outcomes. Prior research on purchase reason has relied on surveys to gather data from users. However, this method is limited in scalability, often focusing on specific products or brands, and may not accurately represent the broader population due to the restricted number of participants involved.
In our work, we propose purchase reason prediction as a novel task for modern AI models. To benchmark potential AI solutions for this new task, we first generate a dataset that consists of real-world explanations of why users make certain purchase decisions for various products. Our approach induces 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 confirm the effectiveness of this approach to obtaining high-quality, personalized purchase reasons and post-purchase experiences. With this novel dataset, we are able to benchmark the purchase reason prediction task using various LLMs. Moreover, we demonstrate how purchase reasons can be valuable for downstream applications, such as marketing-focused user behavior analysis, post-purchase experience and rating prediction in recommender systems, and serving as a new approach to justify recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13417 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience Chen, Tao Zuo, Siqi Li, Cheng Zhang, Mingyang Mei, Qiaozhu Bendersky, Michael Information Retrieval In business and marketing, analyzing the reasons behind buying is a fundamental step towards understanding consumer behaviors, shaping business strategies, and predicting market outcomes. Prior research on purchase reason has relied on surveys to gather data from users. However, this method is limited in scalability, often focusing on specific products or brands, and may not accurately represent the broader population due to the restricted number of participants involved. In our work, we propose purchase reason prediction as a novel task for modern AI models. To benchmark potential AI solutions for this new task, we first generate a dataset that consists of real-world explanations of why users make certain purchase decisions for various products. Our approach induces 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 confirm the effectiveness of this approach to obtaining high-quality, personalized purchase reasons and post-purchase experiences. With this novel dataset, we are able to benchmark the purchase reason prediction task using various LLMs. Moreover, we demonstrate how purchase reasons can be valuable for downstream applications, such as marketing-focused user behavior analysis, post-purchase experience and rating prediction in recommender systems, and serving as a new approach to justify recommendations. |
| title | Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2402.13417 |