Abstract Query reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of query reformulation during Web searching. This article reports results from a study in which we automatically classified the query‐reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next query reformulation. We employed an n‐gram modeling approach to describe the probability of users transitioning from one query‐reformulation state to another to predict their next state. We developed first‐, second‐, third‐, and fourth‐order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all query reformulations; furthermore, the results demonstrate that the first‐ and second‐order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n‐gram approach can be used for improving searching systems and searching assistance.

In this research, we investigate whether one can model online searching as a learning paradigm. We examined the searching characteristics of 41 participants engaged in 246 searching tasks. We classified the searching tasks according to Anderson and Krathwohl's Taxonomy, an updated version of Bloom's taxonomy. Anderson and Krathwohl is a six level categorization of cognitive learning. Research results show that Applying takes the most searching effort as measured by queries per session and specific topics searched per sessions. The categories of Remembering and Understanding, which are lower-order learning levels, exhibit searching characteristics similar to the higher order categories of Evaluating and Creating. It seems that searchers rely primarily on their internal knowledge and use searching primarily as fact checking and verification when engaged in Evaluating and Creating. Implications are that the commonly held notions of Web searchers having simple information goals may not be correct. We discuss the implications for Web searching, including designing interfaces to support exploration and alternate views.

Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million 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 classification to the results from our algorithm. This comparison showed that our automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is generally vague or multi-faceted, pointing to the need to for probabilistic classification. We illustrate how knowledge of searcher intent might be used to enhance future Web search engines.

Investigating whether one can view Web searching as a learning process, we examined the searching characteristics of 41 participants engaged in 246 searching tasks. We classified the searching tasks according an updated version of Bloom.s taxonomy, a six level categorization of cognitive learning. Results show that Applying takes the most searching effort as measured by queries per session and specific topics searched per sessions. The lower level categories of Remembering and Understanding exhibit searching characteristics similar to the higher order learning of Evaluating and Creating. It appears that searchers rely primarily on their internal knowledge for Evaluating and Creating, using searching primarily as fact checking and verification. Implications are that the commonly held notion that Web searchers have simple information needs may not be correct. We discuss the implications for Web searching, including designing interfaces to support exploration.