Paper: Empirical Exploitation of Click Data for Task Specific Ranking

ACL ID D09-1113
Title Empirical Exploitation of Click Data for Task Specific Ranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

There have been increasing needs for task specific rankings in web search such as rankings for specific query segments like long queries, time-sensitive queries, navi- gational queries, etc; or rankings for spe- cific domains/contents like answers, blogs, news, etc. In the spirit of ”divide-and- conquer”, task specific ranking may have potential advantages over generic ranking since different tasks have task-specific fea- tures, data distributions, as well as feature- grade correlations. A critical problem for the task-specific ranking is training data insufficiency, which may be solved by us- ing the data extracted from click log. This paper empirically studies how to appro- priately exploit click data to improve rank function learning in task-specific ranking. The main contributions ...