mlsanityData to 13 Oct 2026

Beyond Rankings: Exploring the Impact of SERP Features on Organic Click-through Rates

arXiv 2306.01785 ↗First posted 31 May 2023cs.IR cs.LG cs.SIcited by 0

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Abstract (arXiv)

Search Engine Result Pages (SERPs) serve as the digital gateways to the vast expanse of the internet. Past decades have witnessed a surge in research primarily centered on the influence of website ranking on these pages, to determine the click-through rate (CTR). However, during this period, the landscape of SERPs has undergone a dramatic evolution: SERP features, encompassing elements such as knowledge panels, media galleries, FAQs, and more, have emerged as an increasingly prominent facet of these result pages. Our study examines the crucial role of these features, revealing them to be not merely aesthetic components, but strongly influence CTR and the associated behavior of internet users. We demonstrate how these features can significantly modulate web traffic, either amplifying or attenuating it. We dissect these intricate interaction effects leveraging a unique dataset of 67,000 keywords and their respective Google SERPs, spanning over 40 distinct US-based e-commerce domains, generating over 6 million clicks from 24 million views. This cross-website dataset, unprecedented in its scope, enables us to assess the impact of 24 different SERP features on organic CTR. Through an ablation study modeling CTR, we illustrate the incremental predictive power these features hold.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 13 of its 49 references that are in the map. First posted on arXiv 31 May 2023.

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References · 13

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,515
  2. 2016Wide & Deep Learning for Recommender Systemscited by 746
  3. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  4. 2019TabNet: Attentive Interpretable Tabular Learningcited by 645
  5. 2017Deep Interest Network for Click-Through Rate Predictioncited by 543
  6. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  7. 2022Why do tree-based models still outperform deep learning on tabular data?cited by 198
  8. 2018Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertisingcited by 59
  9. 2021MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Maskcited by 48
  10. 2016Deep CTR Prediction in Display Advertisingcited by 31
  11. 2022Click-Through Rate Prediction in Online Advertising: A Literature Reviewcited by 31
  12. 2023The Evolution of Web Search User Interfaces -- An Archaeological Analysis of Google Search Engine Result Pagescited by 3
1 more
  1. 2023From 10 Blue Links Pages to Feature-Full Search Engine Results Pages -- Analysis of the Temporal Evolution of SERP Featurescited by 1

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