TY - JOUR TI - Determining the informational, navigational, and transactional intent of Web queries AU - Jansen, Bernard J. AU - Booth, Danielle L. AU - Spink, Amanda T2 - Information Processing & Management AB - In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching. DA - 2008/05/01/ PY - 2008 DO - 10.1016/j.ipm.2007.07.015 DP - ScienceDirect VL - 44 IS - 3 SP - 1251 EP - 1266 J2 - Information Processing & Management LA - en SN - 0306-4573 UR - http://www.sciencedirect.com/science/article/pii/S030645730700163X Y2 - 2018/03/28/23:33:46 ER - TY - JOUR TI - Evaluating Implicit Measures to Improve Web Search AU - Fox, Steve AU - Karnawat, Kuldeep AU - Mydland, Mark AU - Dumais, Susan AU - White, Thomas T2 - ACM Trans. Inf. Syst. AB - Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis (“gene analysis”). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions. DA - 2005/04// PY - 2005 DO - 10.1145/1059981.1059982 DP - ACM Digital Library VL - 23 IS - 2 SP - 147 EP - 168 LA - en SN - 1046-8188 UR - http://doi.acm.org/10.1145/1059981.1059982 Y2 - 2019/01/18/19:48:10 ER - TY - JOUR TI - Real life, real users, and real needs: a study and analysis of user queries on the web AU - Jansen, Bernard J. AU - Spink, Amanda AU - Saracevic, Tefko T2 - Information Processing & Management AB - We analyzed transaction logs containing 51,473 queries posed by 18,113 users of Excite, a major Internet search service. We provide data on: (i) sessions — changes in queries during a session, number of pages viewed, and use of relevance feedback; (ii) queries — the number of search terms, and the use of logic and modifiers; and (iii) terms — their rank/frequency distribution and the most highly used search terms. We then shift the focus of analysis from the query to the user to gain insight to the characteristics of the Web user. With these characteristics as a basis, we then conducted a failure analysis, identifying trends among user mistakes. We conclude with a summary of findings and a discussion of the implications of these findings. DA - 2000/03/01/ PY - 2000 DO - 10.1016/S0306-4573(99)00056-4 DP - ScienceDirect VL - 36 IS - 2 SP - 207 EP - 227 J2 - Information Processing & Management LA - en SN - 0306-4573 ST - Real life, real users, and real needs UR - http://www.sciencedirect.com/science/article/pii/S0306457399000564 Y2 - 2019/01/27/22:52:32 ER -