Contrastive loss[1]是最简单最直观的一种pair-based deep metric learning loss,其思想就是: 1) 选取一对样本对,如果其是正样本对,则其产生的loss就应该等于其特征之间的距离(例如L2 loss);因为我们的期望是他们之间的距离为0,所以凡是大于零的loss都需要被保留。 2) 如果是负样本对,他们之间的距离应该尽可能...
In training, the weight of individual user-item pairs in affecting the user and item embeddings may be determined based on the distance of the particular user-item pair between user embedding and item embedding, as well as the comparative distance for other items of the same type for that ...
listwise 类相较 pointwise、pairwise 对 ranking 的 model 更自然,解决了 ranking 应该基于 query 和 position 问题。 listwise 类存在的主要缺陷是:一些 ranking 算法需要基于排列来计算 loss,从而使得训练复杂度较高,如 ListNet和 BoltzRank。此外,位置信息并没有在 loss 中得到充分利用,可以考虑在 ListNet 和 Li...
listwise 类存在的主要缺陷是:一些 ranking 算法需要基于排列来计算 loss,从而使得训练复杂度较高,如 ListNet和 BoltzRank。此外,位置信息并没有在 loss 中得到充分利用,可以考虑在 ListNet 和 ListMLE 的 loss 中引入位置折扣因子。 2.4 三类方法代表汇总 wiki有很全的三类方法代表: 2019 FastAP [30] listwise Op...
经典的算法有 基于 NN 的 SortNet,基于 NN 的 RankNet,基于 fidelity loss 的 FRank,基于 AdaBoost 的 RankBoost,基于 SVM 的 RankingSVM,基于提升树的 GBRank。 2.2.2 pairwise细则 基于pairwise的方法,在计算目标损失函数的时候,每一次需要基于一个pair的document的预测结果进行损失函数的计算。比如给定一个pair...
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Specific time policy: The system automatically synchronizes data between primary and secondary resources based on a specific time policy. NOTE: If the selected remote device is an OceanStor V3, OceanStor V5, or OceanStor Dorado V3 device, Specific time policy is unavailable. Interval Indicates...
LearningtoRankbasedonImprovedPairwiseLoss Function JiajinWu,ZhihaoYang,YuanLin,HongfeiLin InformationRetrievalLaboratory,DalianUniversityofTechnology,Dalian116024 E-mail:wujiajin@mail.dlut.edu Abstract:Learningtorankisahotissueattheintersectionofmachinelearningandinformationretrieval.Ituses methodsofmachinelearningtoaut...
where the loss function is dependent on more than one training sample (e.g., metric learn- ing, ranking). We present a generic decou- pling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wa...
已经提出了多种损失函数,例如contrastive loss , binomial deviance loss , margin loss , lifted-structure (LS) loss , N-pair loss , triplet loss , multi-similarity (MS) loss。这些基于对的损失之间的主要区别在于对如何在mini-batch中相互作用。在简单的成对损失中,如二项式偏差损失、对比损失和保证金...