TY - GEN
T1 - Identifying popular search goals behind search queries to improve web search ranking
AU - Wang, Ting Xuan
AU - Lu, Wen-Hsiang
PY - 2011/12/28
Y1 - 2011/12/28
N2 - Web users usually have a certain search goal before they submit a search query. However, many laypersons can't transform their search goals into suitable queries. Thus, understanding original search goals behind a query is very important for search engines. In the past decade, many researches focus on classifying search goals behind a query into different search-goal categories. In fact, there may be more than one search goal behind a certain query. We thus propose a novel Popular-Search-Goal-based Search Model to effectively identify search goals by the features extracted from search-result snippets and click-through data. Furthermore, we proposed a Search-Goal-based Ranking Model which exploits the identified search goals to re-rank the search result. The experimental result shows our proposed model can effectively identify the search goals behind a search query (achieve precision of 0.94) and enhance the search result ranking (achieve precision of 0.72 for top-1 returned snippet).
AB - Web users usually have a certain search goal before they submit a search query. However, many laypersons can't transform their search goals into suitable queries. Thus, understanding original search goals behind a query is very important for search engines. In the past decade, many researches focus on classifying search goals behind a query into different search-goal categories. In fact, there may be more than one search goal behind a certain query. We thus propose a novel Popular-Search-Goal-based Search Model to effectively identify search goals by the features extracted from search-result snippets and click-through data. Furthermore, we proposed a Search-Goal-based Ranking Model which exploits the identified search goals to re-rank the search result. The experimental result shows our proposed model can effectively identify the search goals behind a search query (achieve precision of 0.94) and enhance the search result ranking (achieve precision of 0.72 for top-1 returned snippet).
UR - http://www.scopus.com/inward/record.url?scp=84255178545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84255178545&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25631-8_23
DO - 10.1007/978-3-642-25631-8_23
M3 - Conference contribution
AN - SCOPUS:84255178545
SN - 9783642256301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 250
EP - 262
BT - Information Retrieval Technology - 7th Asia Information Retrieval Societies Conference, AIRS 2011, Proceedings
T2 - 7th Asia Information Retrieval Societies Conference, AIRS 2011
Y2 - 18 December 2011 through 20 December 2011
ER -