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arXiv:2609.36614v1 Announce Type: new Abstract: A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which prefe...
arXiv:2604. 08525v2 Announce Type: replace Abstract: Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning.
The paper investigates how role assignment in large language model (LLM) recommenders influences sponsorship bias. By assigning the agent’s principal as either a traveler or a booking platform, the authors find that platform delegation reduces the penalty applied to sponsored listings and weakens consumer skepticism triggered by disclosure. The study also shows that stricter terminology and attribution to the platform widen the divergence in agent evaluations, indicating that current disclosure mandates are insufficient to protect consumers in AI-mediated commerce.
arXiv:2606. 17443v1 Announce Type: new Abstract: Large language models (LLMs) are becoming a major way for consumers to find products, but we do not yet understand how brands compete in this new channel.
The study investigates how Large Language Models (LLMs) acting as surrogate consumers are influenced by marketing pricing cues such as just‑below pricing and promotional framing. Using a tool called "Tool‑Lab" to trace information acquisition, the researchers found that when no cost is imposed, pricing cues rarely mislead LLMs, but when acquisition costs are introduced under a vague goal prompt, LLMs tend to omit important diagnostic attributes and make suboptimal choices similar to human heuristics. The findings suggest that marketing heuristics in AI‑driven shopping are shaped more by storefront information architecture than by inherent LLM limitations.
arXiv:2608. 08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents.