Feature engineering on event-centric surrogate documents to improve search results
Wenhui Liao, Isabelle Moulinier · 2009
We investigate the task of re-ranking search results based on query log information. Prior work has considered this problem as either the task of learning document rankings of using features based on user behavior, or as the task of enhancing documents and queries using log data. Our contribution combines both. We distill log information into event-centric surrogate documents (ESDs), and extract features from these ESDs to be used in a learned ranking function. Our experiments on a legal corpus demonstrate that features engineered on surrogate documents lead to improved rankings, in particular when the original ranking is of poor quality.