LLM-Based Product Recommendation with Prospect Theoretic Self Alignment Strategy

Manying Zhang, Zehua Cheng, Damien Nouvel

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.

External IDs:dblp:conf/ranlp/ZhangCN25