Understanding user goals in web search

By Daniel E. Rose, Danny Levinson. The Web Conference, 2004

1,154 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e42325e8adfa05f72a4de7c264d679dc4a70134b

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In Information Systems

Citations in the corpus

ExpertQA: Expert-Curated Questions and Attributed Answers (2023, positive): “We formulate these question types broadly based on existing work that attempts to classify information needs (Rose and Levinson, 2004).”
Twitter power: Tweets as electronic word of mouth (2009, methodology): “In addition to determining whether these tweets mentioned the brands, we classified 2,700 tweets into general categories, similar to the work outlined previously (Broder, 2002; Jansen, Booth, & Spink, 2008; Rose & Levinson, 2004) classifying Web queries.”
Interpreting informational cues: An explorative study on information use among prospective homebuyers (2009, neutral): “In addition to determining whether these tweets mentioned the brands, we classified 2,700 tweets into general categories, similar to the work outlined previously (Broder, 2002; Jansen, Booth, & Spink, 2008; Rose & Levinson, 2004 ) classifying Web queries.”
Toward an Integrated Framework of Multiple Text Use (2019, background): “First, the engagement of these strategies has been found to have a strong heuristic component (Lim, 2013; Rose & Levinson, 2004).”
Tailoring click models to user goals (2009, background): “On the other hand, the authors were aware of two general categories of query terms in existing literatures [2, 15].”
How does search behavior change as search becomes more difficult? (2010, background): “All tasks (both the easy filler tasks and the difficult tasks) were directed closed informational tasks ("I want to get an answer to a question that has a single, unambiguous answer" [20]).”
Representation of the Online Tourism Domain in Search Engines (2008, background): “Given that user queries are generally short and most users will not examine more than the first three pages of search results, it is crucial for search engines to provide the most relevant search results in an efficient fashion (Henzinger 2007; Rose and Levinson 2004; Spink and Jansen 2004).”
An anatomy for neural search engines (2019, background): “The literature provides a good framework to understand what are the user goals when web searching [Rose and Levinson, 2004], and they can be broadly classified into “precision-oriented” or “recall-oriented”.”
Essential Pages (2009, background): “Looking from a user’s perspective, within an existing classiﬁcation of Web-search queries [10], [11] we focus on informational, learning-type queries – where the user wants to know a speciﬁc detail or the entire breadth of knowledge available on the Web for a speciﬁc query (e.g., rhododendron ).”
Named entity recognition in query (2009, background): “Query classification falls into two groups: (1) classification according to search intent, such as informational, navigational or transactional [7, 21, 16]; (2) classification according to semantics of query, such as “Shopping” or “Living” [24, 1].”
Product Question Intent Detection using Indicative Clause Attention and Adversarial Learning (2018, background): “Rose and Levinson [24] further extended this intent-based web query categorization to a hierarchical taxonomy.”
Developing a Framework for Assessing Information Quality on the World Wide Web (2005, background): “The end-user should be known so that cognitive, sociological and quality choice processes are better understood (Rose & Levinson, 2004 ).”
A large scale study of wireless search behavior: Google mobile search (2006, background): “Broder [1] and Rose [10] have each manually classified small samples of log data to determine the user-needs driving web queries.”
Understanding Short-term Changes in Online Activity Sessions (2017, background): “Sessions of activity have been constructed to understand online behavior especially in the context of search and web surﬁng [22, 19, 3].”
To search or to ask: the routing of information needs between traditional search engines and social networks (2014, background): “’s schema: 1) navigational information needs [16, 28], which are typically expressed by queries such as “cscw 2014” and “gmail”, and 2) exploratory information needs [19, 35], which are defined by their open-ended and multifaceted nature.”
What are you looking for?: an eye-tracking study of information usage in web search (2007, background): “Two or three broad classes of Web search tasks have been identified in the literature [1, 22] and used in various studies on Web search [12, 15, 17].”
Role of social media in online travel information search (2010, background): “Search engines, to a great extent, define the practical boundaries of the online tourism domain and dictate the way it is represented (Henzinger, 2007; Prescott, 2006; Rose & Levinson, 2004; Spink & Jansen, 2004).”
A picture of search (2006, result): “Figure 3.3 highlights the fact that some user queries are navigational in intent, not informational; our own estimate of 21% is in line with prior work [9, 10 ].”