Existing learning to rank(LTR)models rely on the scores output by models to represent the partial order among docu-ments.Considering the limitation of treating scores as deterministic values,this paper proposes a probability distribution optimization method for the LTR model,which introduces the uncertainty of the ranking score.It smooths the scores in the form of probability distributions,thereby transforming the comparison of ranking scores into the probability estimation of score partial orders.The pro-posed method is applied to LTR models such as RankNet,LambdaRank,and LambdaMART.It effectively bridges the gap between the modeled probability and the target probability,leading to optimization of the LTR models.The paper conducts experiments on multiple large-scale real datasets,and the experimental results show that the optimized models outperform the original ones,which validates the effectiveness of the proposed method.
information retrievallearning to rankprobability distribution
NETL
NSTL
万方数据
