Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation

arXiv:2403.00844v1 Announce Type: cross
Abstract: Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking metrics are the gold standard for optimization, they suffer from significant computational overhead. Alternatively, the more efficient accuracy and AUC metrics often fall short of capturing the true targets of recommendation tasks, leading to suboptimal performance. To overcome this dilemma, we propose a new optimization metric, Lower-Left Partial AUC (LLPAUC), which …

abstract accuracy arxiv auc building computational cs.ir cs.lg metrics optimization practical ranking recommendation recommendation systems scale standard systems type