Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

arXiv:2310.10462v2 Announce Type: replace
Abstract: Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and …

abstract advertising arxiv business cs.lg focus framework learn learning-to-rank online advertising ranking recommendation recommendation systems scale systems type