Language Models for Page-Level Layout Decisions in E-commerce Search

Key points

• While traditional search engines return a ranked list of results, modern E-commerce search pages increasingly incorporate recommender system modules -- for example, secondary stacks that surface alternative product groupings at specific positions.1
• When introduced appropriately, secondary stacks can improve user engagement; however, suboptimal placement may disrupt browsing flow and degrade the primary results.1
• We investigate language models as scalable evaluators by comparing direct prompt-based, prompt-derived feature, and representation-based methods.1
• Our results show that representation-based approaches consistently outperform prompt-based judging in predicting user engagement, suggesting they provide a reliable foundation for offline layout evaluation in E-commerce search.1

Sources (1)

• [1]Language Models for Page-Level Layout Decisions in E-commerce Search
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:19 PM

  E-commerce search pages are critical touchpoints for millions of online shoppers.

  While traditional search engines return a ranked list of results, modern E-commerce search pages increasingly incorporate recommender system modules -- for example, secondary stacks that surface alternative product groupings at specific positions.

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