Robust Models of Mouse Movement on Dynamic Web Search Results Pages

Understanding how users examine result pages across a broad range of information needs is critical for search engine design. Cursor movements can be used to estimate visual attention on search engine results page (SERP) components, including traditional snippets, aggregated results, and advertisements. However, these signals can only be leveraged for SERPs where cursor tracking was enabled, limiting their utility for informing the design of new SERPs. In this work, we develop robust, log-based mouse movement models capable of estimating searcher attention on novel SERP arrangements. These models can help improve SERP design by anticipating searchers’ engagement patterns given a proposed arrangement. We demonstrate the efficacy of our method using a large set of mouse-tracking data collected from two independent commercial search engines.

Speaker Details

Fernando Diaz is a research scientist at Microsoft Research New York City. His primary research interest is formal information retrieval models. Fernando’s research experience includes distributed information retrieval approaches to web search, interactive and faceted retrieval, mining of temporal patterns from news and query logs, cross-lingual information retrieval, graph-based retrieval methods, and synthesizing information from multiple corpora. Fernando received his PhD from the University of Massachusetts Amherst in 2008. His work on federation won the best paper awards at the SIGIR 2009, WSDM 2009, and ECIR 2011 conferences as well as a best paper nomination at SIGIR 2011.

Date:
Speakers:
Fernando Diaz
Affiliation:
Microsoft Research New York City

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      Fernando Diaz

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      Jeff Running