arXiv:2607. 12056v1 Announce Type: new Abstract: Online shopping is increasingly shifting toward a model in which AI agents independently search for products, compare options, evaluate constraints, and carry out parts of the purchasing process for users.
arXiv:2606. 12924v1 Announce Type: new Abstract: We present a modular two-agent simulation framework for evaluating conversational shopping assistant architectures.
arXiv:2608. 04205v1 Announce Type: new Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale.
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost.
arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
arXiv:2606. 17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated implicitly in the query, recorded in a profile, or revealed only when the right question is asked.
arXiv:2608. 14068v1 Announce Type: cross Abstract: Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a stricter requirement: recommendations must be drawn only from a merchant's fixed catalog, without web search or unsupported product claims.
arXiv:2607. 13998v1 Announce Type: cross Abstract: The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms.
arXiv:2606. 00051v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in business settings remains uncertain.
arXiv:2510. 04452v3 Announce Type: replace-cross Abstract: Computer use agents (or "agents") are generative AI that automates actions within user interfaces from user commands.
arXiv:2605. 12887v2 Announce Type: replace-cross Abstract: Web-enabled LLM agents are changing how online information influences search outcomes.
arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.