Query chains: Learning to rank from implicit feedback

@misc{citeulike:2252778, abstract = {This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference...}, author = {Radlinski, F. and Joachims, T. }, citeulike-article-id = {2252778}, keywords = {clickthrough-data, implicit-feedback, learning-to-rank, search-logs, search-session}, posted-at = {2008-03-21 08:08:49}, priority = {2}, title = {Query chains: Learning to rank from implicit feedback}, url = {http://citeseer.ist.psu.edu/733685.html}, year = {2005} }

TY - GEN ID - citeulike:2252778 L3 - citeulike-article-id:2252778 TI - Query chains: Learning to rank from implicit feedback N2 - This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference... KW - clickthrough-data KW - implicit-feedback KW - learning-to-rank KW - search-logs KW - search-session AU - Radlinski, F AU - Joachims, T PY - 2005/// UR - http://citeseer.ist.psu.edu/733685.html ER -