Semi-supervised learning listwise ranking functions for document retrieval
Long Yue-jin · Journal of Computer Applications · 2011
An iterative co-ranking algorithm,which aimed to extend learning to rank from a supervised setting into a semi-supervised setting,was proposed.The approach employed two listwise rankers to identify document permutations for an unlabeled query.In particular,the use of likelihood listwise loss was introduced to measure the difference score of two learners for a given query.The unlabeled query which showed significant difference score was then chosen for constructing the newly training dataset at next iteration,and its ideal document permutation for a listwise ranker was defined by another learner.The experimental results show that the proposed method can improve the ranking performance of supervised listwise ranking algorithm on the public dataset LETOR.In addition,the labeling ratio was also discussed.