Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
80 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience (2402.13417v3)

Published 20 Feb 2024 in cs.IR

Abstract: 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.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. Evaluating the relationships among attitude toward business, product satisfaction, experience, and search effort. Journal of Marketing Research, 16(3):394–400.
  2. Daniëlle NM Bleize and Marjolijn L Antheunis. 2019. Factors influencing purchase intent in virtual worlds: a review of the literature. Journal of Marketing Communications, 25(4):403–420.
  3. Tung-Zong Chang and Albert R Wildt. 1994. Price, product information, and purchase intention: An empirical study. Journal of the Academy of Marketing science, 22:16–27.
  4. Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference, WWW ’18, page 1583–1592.
  5. Dynamic explainable recommendation based on neural attentive models. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI’19/IAAI’19/EAAI’19. AAAI Press.
  6. Brand equity, brand preference, and purchase intent. Journal of advertising, 24(3):25–40.
  7. Automatic generation of natural language explanations. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion, IUI ’18 Companion, New York, NY, USA. Association for Computing Machinery.
  8. R Flesch. 1948. A new readability yardstick. Journal of Applied Psychology, 32(3):221–233.
  9. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
  10. Gemini Team. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805.
  11. Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM Conference on Recommender Systems, RecSys ’22, page 299–315, New York, NY, USA. Association for Computing Machinery.
  12. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232.
  13. Is chatgpt a good translator? yes with gpt-4 as the engine. arXiv preprint arXiv:2301.08745.
  14. Tom Kocmi and Christian Federmann. 2023. Large language models are state-of-the-art evaluators of translation quality. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 193–203, Tampere, Finland. European Association for Machine Translation.
  15. Extra: Explanation ranking datasets for explainable recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 2463–2469, New York, NY, USA. Association for Computing Machinery.
  16. Personalized transformer for explainable recommendation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4947–4957, Online. Association for Computational Linguistics.
  17. On the relationship between explanation and recommendation: Learning to rank explanations for improved performance. ACM Trans. Intell. Syst. Technol., 14(2).
  18. Personalized prompt learning for explainable recommendation. ACM Trans. Inf. Syst., 41(4).
  19. Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, page 345–354, New York, NY, USA. Association for Computing Machinery.
  20. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74–81.
  21. Is chatgpt a good recommender? a preliminary study.
  22. Explore, exploit, and explain: personalizing explainable recommendations with bandits. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18, page 31–39, New York, NY, USA. Association for Computing Machinery.
  23. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 188–197, Hong Kong, China. Association for Computational Linguistics.
  24. Richard L Oliver and John E Swan. 1989. Equity and disconfirmation perceptions as influences on merchant and product satisfaction. Journal of consumer research, 16(3):372–383.
  25. OpenAI. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  26. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318.
  27. Marsha L Richins and Peter H Bloch. 1991. Post-purchase product satisfaction: Incorporating the effects of involvement and time. Journal of Business Research, 23(2):145–158.
  28. M Joseph Sirgy. 1985. Using self-congruity and ideal congruity to predict purchase motivation. Journal of business Research, 13(3):195–206.
  29. Mildred C Templin. 1957. Certain language skills in children; their development and interrelationships. Child Welfare Monograph, 26.
  30. Nava Tintarev and Judith Masthoff. 2015. Explaining Recommendations: Design and Evaluation, pages 353–382. Springer US, Boston, MA.
  31. Is ChatGPT a good NLG evaluator? a preliminary study. In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 1–11, Singapore. Association for Computational Linguistics.
  32. Explainable recommendation via multi-task learning in opinionated text data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’18, page 165–174, New York, NY, USA. Association for Computing Machinery.
  33. A reinforcement learning framework for explainable recommendation. In 2018 IEEE International Conference on Data Mining (ICDM), pages 587–596.
  34. Automated evaluation of personalized text generation using large language models. arXiv preprint arXiv:2310.11593.
  35. Understanding user behavior for document recommendation. In Proceedings of The Web Conference 2020, WWW ’20, page 3012–3018, New York, NY, USA. Association for Computing Machinery.
  36. Markus Zanker. 2012. The influence of knowledgeable explanations on users’ perception of a recommender system. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, page 269–272, New York, NY, USA. Association for Computing Machinery.
  37. Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics, 12:39–57.
  38. Explainable recommendation: A survey and new perspectives. Found. Trends Inf. Retr., 14(1):1–101.
  39. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14, page 83–92, New York, NY, USA. Association for Computing Machinery.
  40. Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14, page 1027–1030, New York, NY, USA. Association for Computing Machinery.
  41. Siren’s song in the ai ocean: A survey on hallucination in large language models. arXiv preprint arXiv:2309.01219.
  42. A survey of large language models. arXiv preprint arXiv:2303.18223.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Tao Chen (397 papers)
  2. Siqi Zuo (3 papers)
  3. Cheng Li (1094 papers)
  4. Mingyang Zhang (56 papers)
  5. Qiaozhu Mei (68 papers)
  6. Michael Bendersky (63 papers)
Citations (2)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets