Academic

AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping

arXiv:2602.12315v1 Announce Type: cross Abstract: The proliferation of e-commerce has made web shopping platforms key gateways for customers navigating the vast digital marketplace. Yet this rapid expansion has led to a noisy and fragmented information environment, increasing cognitive burden as shoppers explore and purchase products online. With promising potential to alleviate this challenge, agentic systems have garnered growing attention for automating user-side tasks in web shopping. Despite significant advancements, existing benchmarks fail to comprehensively evaluate how well agentic systems can curate products in open-web settings. Specifically, they have limited coverage of shopping scenarios, focusing only on simplified single-platform lookups rather than exploratory search. Moreover, they overlook personalization in evaluation, leaving unclear whether agents can adapt to diverse user preferences in realistic shopping contexts. To address this gap, we present AgenticShop, th

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Sunghwan Kim, Ryang Heo, Yongsik Seo, Jinyoung Yeo, Dongha Lee · · 1 min read · 17 views
#cs.IR #cs.AI

arXiv:2602.12315v1 Announce Type: cross Abstract: The proliferation of e-commerce has made web shopping platforms key gateways for customers navigating the vast digital marketplace. Yet this rapid expansion has led to a noisy and fragmented information environment, increasing cognitive burden as shoppers explore and purchase products online. With promising potential to alleviate this challenge, agentic systems have garnered growing attention for automating user-side tasks in web shopping. Despite significant advancements, existing benchmarks fail to comprehensively evaluate how well agentic systems can curate products in open-web settings. Specifically, they have limited coverage of shopping scenarios, focusing only on simplified single-platform lookups rather than exploratory search. Moreover, they overlook personalization in evaluation, leaving unclear whether agents can adapt to diverse user preferences in realistic shopping contexts. To address this gap, we present AgenticShop, the first benchmark for evaluating agentic systems on personalized product curation in open-web environment. Crucially, our approach features realistic shopping scenarios, diverse user profiles, and a verifiable, checklist-driven personalization evaluation framework. Through extensive experiments, we demonstrate that current agentic systems remain largely insufficient, emphasizing the need for user-side systems that effectively curate tailored products across the modern web.

Executive Summary

The article 'AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping' addresses the challenges posed by the rapid expansion of e-commerce, which has led to a fragmented and noisy digital marketplace. The authors highlight the cognitive burden on shoppers and the potential of agentic systems to automate user-side tasks. They introduce AgenticShop, a benchmark designed to evaluate agentic systems' ability to curate products in open-web environments, focusing on realistic shopping scenarios and personalized user preferences. The study reveals that current agentic systems are insufficient, underscoring the need for more effective user-side systems.

Key Points

  • ▸ The proliferation of e-commerce has increased the cognitive burden on shoppers due to a noisy and fragmented digital marketplace.
  • ▸ Existing benchmarks for agentic systems are limited, focusing on simplified single-platform lookups rather than exploratory search and personalization.
  • ▸ AgenticShop is introduced as the first benchmark for evaluating agentic systems on personalized product curation in open-web environments.
  • ▸ The benchmark features realistic shopping scenarios, diverse user profiles, and a verifiable, checklist-driven personalization evaluation framework.
  • ▸ Current agentic systems are found to be largely insufficient, emphasizing the need for more effective user-side systems.

Merits

Comprehensive Benchmark

AgenticShop provides a comprehensive evaluation framework that addresses the gaps in existing benchmarks, focusing on realistic shopping scenarios and personalized user preferences.

Realistic Evaluation

The benchmark includes diverse user profiles and a verifiable, checklist-driven personalization evaluation framework, making it more applicable to real-world shopping contexts.

Demerits

Limited Scope

While the benchmark is comprehensive, it may not cover all possible shopping scenarios and user preferences, potentially limiting its generalizability.

Technological Limitations

The study highlights the insufficiency of current agentic systems, but does not provide detailed solutions or recommendations for improving these systems.

Expert Commentary

The article 'AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping' presents a timely and rigorous analysis of the challenges faced by shoppers in the digital marketplace. The introduction of AgenticShop as a benchmark for evaluating agentic systems is a significant contribution to the field. The study's emphasis on realistic shopping scenarios and personalized user preferences addresses critical gaps in existing benchmarks. However, the findings also highlight the current limitations of agentic systems, underscoring the need for further research and development. The practical implications of this study are substantial, as more effective agentic systems could greatly enhance the shopping experience by reducing cognitive burden and providing tailored product recommendations. From a policy perspective, the study raises important questions about the ethical use of personalization and the need for industry standards to ensure the fairness and effectiveness of these systems. Overall, the article provides a valuable framework for future research and development in the area of agentic systems and personalized web shopping.

Recommendations

  • ✓ Further research should focus on developing more advanced agentic systems that can effectively curate personalized products across diverse shopping scenarios.
  • ✓ E-commerce platforms should invest in and adopt these advanced agentic systems to enhance the shopping experience and meet consumer needs.
  • ✓ Policymakers should consider regulations and industry standards to ensure the ethical use of personalization in agentic systems, protecting consumer privacy and data security.

Sources

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