Determining the user intent of web search engine queries

Abstract

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.

Contextual Search: A Computational Framework

“…Ingwersen [77] reports on evaluation from both an information seeking and retrieval and operational point of view. Jansen and Spink [85] use logs (nine major commercial search engine anonymized and well prepared query logs); see also Jansen and Spink [85]; Jansen et al [82,83,84]. Jansen [81]'s paper is a useful side-effect is the public availability of the data set.…”

E–Government meets Search Computing

“…The design of novel search systems and interfaces is backed by several studies aimed at understanding how users' search behaviour on the Web [20][23] [24][29] [31]. A review of approaches to search log data mining and Web search investigation is in [2] and [21].…”

Information Extraction from Hypertext Mark-Up Language Web Pages

“…Jansen et al [1] proposed a model to improve web search engines by classifying user search based on intention in terms of the type of content specified and operationalize these classifications with defining characteristics. The limitation of this study is that they assigned each query to one and only one category.…”