Beyond clicks: query reformulation as a predictor of search satisfaction

  • Authors:

  • Ahmed Hassan;Xiaolin Shi;Nick Craswell;Bill Ramsey

  • Affiliations:

  • Microsoft Research, Redmond, WA, USA;Microsoft Bing, Redmond, WA, USA;Microsoft Bing, Redmond, WA, USA;Microsoft Bing, Redmond, WA, USA

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

To understand whether a user is satisfied with the current search results, implicit behavior is a useful data source, with clicks being the best-known implicit signal. However, it is possible for a non-clicking user to be satisfied and a clicking user to be dissatisfied. Here we study additional implicit signals based on the relationship between the user's current query and the next query, such as their textual similarity and the inter-query time. Using a large unlabeled dataset, a labeled dataset of queries and a labeled dataset of user tasks, we analyze the relationship between these signals. We identify an easily-implemented rule that indicates dissatisfaction: that a similar query issued within a time interval that is short enough (such as five minutes) implies dissatisfaction. By incorporating additional query-based features in the model, we show that a query-based model (with no click information) can indicate satisfaction more accurately than click-based models. The best model uses both query and click features. In addition, by comparing query sequences in successful tasks and unsuccessful tasks, we observe that search success is an incremental process for successful tasks with multiple queries.