During selection of expansion terms from both clicked documents and queries in log, current methods never consider the large amount of irrelevant feedbacks in records of new queries, which leads to the inconsistency between users' intents and selected expansion terms. To solve this problem, this paper integrated the ranking model with the recognition of irrelevant document content according to users' study processes, so it can reduce the weights of irrelevant expansion terms. The experimental results show that this method can measure relevancy between query and document terms better, especially in the situation when records have more irrelevant feedbacks, and can select expansion terms accorded with users' intent better.
['Bo Zhang', 'Bin Zhang', 'Shubo Zhang', 'Daming Sun']
Selecting expansion terms based on path-constrained term-relationship graphs