Automatic identification of user goals in Web search

By Uichin Lee, Zhenyu Liu, Junghoo Cho. The Web Conference, 2005

556 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3b23535798d62e93077706038650224aeeaba539

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In Information Systems

Citations in the corpus

Semantic Matching in Search (2014, background): “Query classification [97, 113, 13, 161, 119] and document classification should be helpful for addressing query document mismatch.”
Discovering and understanding word level user intent in Web search queries (2015, background): “Past research has mostly focused on inferring the intent of the query as a whole, and the most generic intent classes were found to be informational, navigational and transactional [4, 5, 6].”
Estimating Embedding Vectors for Queries (2016, background): “In the ﬁrst, the labels are query types, such as navigational queries, informational queries, and transactional queries, e.g., [4, 25].”
What are you looking for?: an eye-tracking study of information usage in web search (2007, methodology): “, in the trivial case, a button) or using automatic classification [14, 15].”
Learning query intent from regularized click graphs (2008, methodology): “To avoid their adverse effect on our learning algorithms, we identify navigational queries based on measures proposed in [11] and remove them from our click graphs.”
Determining the informational, navigational, and transactional intent of Web queries (2008, methodology): “Lee, Liu, and Cho (2005) automatically classified informational and navigational queries using 50 queries collected from computer science students at a US university.”
Personalized news recommendation based on click behavior (2010, methodology): “For instance, Qiu and Cho [13] presented a formal framework and a method to automatically learn user interest based on past click history.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, background): “In particular, works in [114, 115, 131, 177, 5, 6, 4, 118, 237, 178], show techniques operating on different features extracted from query logs.”
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].”
KDD CUP-2005 report: facing a great challenge (2005, background): “Given the exponential growth of information s availability in electronic form, search becomes one of the most important and effective approaches to finding correct/relevant information to serve our needs.”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “Contrarily, for the query “Google”, which is a typical navigational query as defined in [3, 17], almost all of the users are consistently selecting results to redirect to Google’s homepage, and therefore none of the personalized strategies could provide significant benefits to users.”
Tailoring click models to user goals (2009, methodology): “Following a similar approach as in [11], we can derive a single numeric feature based on these statistics and define a cut-off threshold.”
Towards Large-Scale Collaborative Planning: Answering High-Level Search Queries Using Human Computation (2011, background): “2006; Lee, Liu, and Cho 2005; Rose and Levinson 2004).”