TY - CHAP
T1 - Assessing and predicting vertical intent for web queries
AU - Zhou, Ke
AU - Cummins, Ronan
AU - Halvey, Martin
AU - Lalmas, Mounia
AU - Jose, Joemon M.
PY - 2012
Y1 - 2012
N2 - Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.
AB - Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.
KW - web searching
KW - diverse vertical content
KW - inter-assessor agreement
KW - search engine log
U2 - 10.1007/978-3-642-28997-2_50
DO - 10.1007/978-3-642-28997-2_50
M3 - Chapter (peer-reviewed)
SN - 9783642289965
T3 - Lecture Notes in Computer Science
SP - 499
EP - 502
BT - Advances in Information Retrieval
PB - Springer
ER -