In this work, we propose a personalized pairwise novelty weighting framework for BPR loss function, which covers the limitations of BPR and effectively improves novelty with negligible decrease in accuracy. Base model will be guided by the novelty-aware loss weights to learn user preference and to...
of −0.1 due to its low MSE value, but this can be effectively realized by the classification loss function. Additionally, the ranking information contained in the hidden representation of a paired sample may be further reinforced by the auxiliary task to improve the ranking ability of PBCNet....
First, we use a ranking loss, Lv-rk(V) = log 1 + e ,(Fcls(ga )−Fcls(ga)) (12) a∈A,a ∈A where F cls(ga) is the probability logit without passing it through the sigmoid function. Since set-supervised action recognition is a multi-label ...
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 ...