Ready to buy or just browsing?: detecting web searcher goals from interaction data

  • Authors:

  • Qi Guo;Eugene Agichtein

  • Affiliations:

  • Emory University, Atlanta, GA, USA;Emory University, Atlanta, GA, USA

  • Venue:
  • Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2010

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Abstract

An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new class of search behavior models that also exploit fine-grained user interactions with the search results. We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user's search goals. Potential applications include automatic search evaluation, improving search ranking, result presentation, and search advertising. We describe extensive experimental evaluation over both controlled user studies, and logs of interaction data collected from hundreds of real users. The results show that our method is more effective than the current state-of-the-art techniques, both for detection of searcher goals, and for an important practical application of predicting ad clicks for a given search session.