Pseudo test collections for learning web search ranking functions

Abstract

Test collections are the primary drivers of progress in information retrieval. They provide yardsticks assessing the effectiveness of ranking functions in an automatic, rapid, and repeatable fashion and serve as training data learning to rank models. However, manual construction of test collections tends to be slow, labor-intensive, and expensive. This paper examines the feasibility of constructing web search test collections in a completely unsupervised manner given only a large web corpus as input. Within our proposed framework, anchor text extracted from the web graph is treated as a pseudo query log from which pseudo queries are sampled. For each pseudo query, a set of relevant and non-relevant documents are selected using a variety of web-specific features, including spam and aggregated anchor text weights. The automatically mined queries and judgments form a pseudo test collection that can be used training ranking functions. Experiments carried out on TREC web track data show that learning to rank models trained using pseudo test collections outperform an unsupervised ranking function and are statistically indistinguishable from a model trained using manual judgments, demonstrating the usefulness of our approach in extracting reasonable quality training data for free.