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.

The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance with implicit user feedback. Unbiased Learning to Rank~(ULTR) algorithms are then proposed to learn an unbiased ranking model with biased click data. However, most existing algorithms are specifically designed to mitigate position-related bias, e.g., trust bias, without considering biases induced by other features in search result page presentation(SERP). For example, the multimedia type may generate attractive bias. Unfortunately, those biases widely exist in industrial systems and may lead to an unsatisfactory search experience. Therefore, we introduce a new problem, i.e., whole-page Unbiased Learning to Rank(WP-ULTR), aiming to handle biases induced by whole-page SERP features simultaneously. It presents tremendous challenges. For example, a suitable user behavior model (user behavior hypothesis) can be hard to find; and complex biases cannot be handled by existing algorithms. To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, BAL, to automatically discover and mitigate the biases from multiple SERP features with no specific design. Experimental results on a real-world dataset verify the effectiveness of the BAL.

The SEO landscape is constantly evolving, and marketers have to adapt to this evolution. Major Google updates have rendered manipulative SEO practices somewhat obsolete. But there is still a lot that marketers have to do to ensure that their websites are highly visible in relevant searches. This makes it crucial that you keep yourself updated on the latest changes and the future predictions in regards to SEO. And what better way to get your predictions and advice than from the top experts in search engine optimization. We are going to see more "knowledge-based trust" and the Google knowledge vault returning from Google, and the Semantic Web is going to make featured snippets and structured snippets more compelling and interesting to people doing marketing on the Web. People are going to be advertising in knowledge graphs, where entities actions are going to be the focus of their campaigns. "I am an entity," is going to be a goal for many more on the Web, because it will matter even more. My main prediction for 2018 is that traditional SEO real estate, like ranking 1-10, will continue to shrink. Increasingly, you will be fighting for Featured Snippets and Featured Videos. If you do not put a strategy in place to rank for featured snippets, you are going to be at a big disadvantage. Featured snippets, answer boxes and other SERP features are at a tipping point. Visitors now expect to see the answer right there in the search results. So we should expect that clickthrough rates will decline in general.