The re-ranking algorithm is thus completely independent of the base model. Eventually, these frameworks are essentially limited by the base model and the separated 2 stages cause greater complication and inefficiency in providing novel suggestions. In this work, we propose a personalized pairwise ...
Since set-supervised action recognition is a multi-label learning problem, the ranking loss resolves the positive and negative sample imbalance. However, its drawback is requiring to tune a threshold on the probability logit to separate the positive from negative a...
Lossentropyis entropy loss and Losstotalis final loss. The aim of the introduction of entropy loss is to penalize the predictions with low errors but completely wrong ranking. For example, it is difficult for the regression loss function to penalize a sample with a label of 0.1 and a predicte...
provedDL [1] and DCSL [35], which chooses the negative samples with large loss on current model, we try to increase E[Loss|θ, r] by adjusting r, and the objective function in the training phase can be written in a minimax form: min{max E[Loss|θ, r]} θr (18) To satisfy ...
However, humans are biased: if the algorithm that takes as input the human feedback doe...doi:10.1007/s10618-024-01024-zFerraraAntonioBonchiFrancescoFabbriFrancescoKarimiFaribaWagnerClaudiaSpringer USNew YorkData Mining and Knowledge Discovery