- Behavioral dynamics from the SERP's perspective
- What are failed SERPs and how to fix them?
- 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
- Book/source title
- Book/source subtitle
- proceedings of the 24th ACM International Conference on Information and Knowledge Management : October 19-23, 2015, Melbourne, Australia
- Pages (from-to)
- Number of pages
- New York: The Association for Computing Machinery
- ISBN (electronic)
- Document type
- Conference contribution
- Faculty of Science (FNWI)
Interfacultary Research Institutes
- Informatics Institute (IVI)
Institute for Logic, Language and Computation (ILLC)
Web search is always in a state of flux: queries, their intent, and the most relevant content are changing over time, in predictable and unpredictable ways. Modern search technology has made great strides in keeping up to pace with these changes, but there remain cases of failure where the organic search results on the search engine result page (SERP) are outdated, and no relevant result is displayed. Failing SERPs due to temporal drift are one of the greatest frustrations of web searchers, leading to search abandonment or even search engine switch. Detecting failed SERPs timely and providing access to the desired out-of-SERP results has huge potential to improve user satisfaction. Our main findings are threefold: First, we refine the conceptual model of behavioral dynamics on the web by including the SERP and defining (un)successful SERPs in terms of observable behavior. Second, we analyse typical patterns of temporal change and propose models to predict query drift beyond the current SERP, and ways to adapt the SERP to include the desired results. Third, we conduct extensive experiments on real world search engine traffic demonstrating the viability of our approach. Our analysis of behavioral dynamics at the SERP level gives new insight in one of the primary causes of search failure due to temporal query intent drifts. Our overall conclusion is that the most detrimental cases in terms of (lack of) user satisfaction lead to the largest changes in information seeking behavior, and hence to observable changes in behavior we can exploit to detect failure, and moreover not only detect them but also resolve them.
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