@inproceedings{zhang-etal-2025-llm-based-product,
    title = "{LLM}-Based Product Recommendation with Prospect Theoretic Self Alignment Strategy",
    author = "Zhang, Manying  and
      Cheng, Zehua  and
      Nouvel, Damien",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.165/",
    pages = "1430--1436",
    abstract = "Accurate and personalized product recommendation is central to user satisfaction in e-commerce. However, a persistent language gap often exists between user queries and product titles or descriptions. While traditional user behavior-based recommenders and LLM-based Retrieval-Augmented Generation systems typically optimize for maximum likelihood objectives, they may struggle to bridge this gap or capture users' true intent. In this paper, we propose a strategy based on Prospect Theoretic Self-Alignment, that reframes LLM-based recommendations as a utility-driven process. Given a user query and a set of candidate products, our model acts as a seller who anticipates latent user needs and generates product descriptions tailored to the user{'}s perspective. Simultaneously, it simulates user decision-making utility to assess whether the generated content would lead to a purchase. This self-alignment is achieved through a training strategy grounded in Kahneman {\&} Tversky{'}s prospect theory, ensuring that recommendations are optimized for perceived user value rather than likelihood alone. Experiments on real-world product data demonstrate substantial improvements in intent alignment and recommendation quality, validating the effectiveness of our approach in producing personalized and decision-aware recommendations."
}