Modeling score distributions for information retrieval
Keshi Dai · 2012
When a user submits a query to a search engine, the search engine computes a score for each document according to its relevance to the query, and ranks the documents based on their scores. Due to the complexity of the modern search engine, the score itself is not sufficient for the information retrieval application requiring combining different ranked lists. Inferring the score distributions for relevant and non-relevant documents and estimating the probability of relevance become imperative. In this thesis, we address two major research questions: (1) How to model score distributions in a more accurate manner for relevant and non-relevant documents? (2) How can score distributions be better inferred in practice when the relevance information is absent?