Ensemble models in E-commerce combine predictions from multiple sub-models for ranking and revenue improvement. Industrial ensemble models are typically deep neural networks, following the supervised learning paradigm to infer conversion rate given inputs from sub-models. However, this process has the following two problems. Firstly, the point-wise scoring approach disregards the relationships between items and leads to homogeneous displayed results, while diversified display benefits user experience and revenue. Secondly, the learning paradigm focuses on the ranking metrics and does not directly optimize the revenue. In our work, we propose a new Learning-To-Ensemble (LTE) framework RAEGO, which replaces the ensemble model with a contextual Rank Aggregator (RA) and explores the best weights of sub-models by the Evaluator-Generator Optimization (EGO). To achieve the best online performance, we propose a new rank aggregation algorithm TournamentGreedy as a refinement of classic rank aggregators, which also produces the best average weighted Kendall Tau Distance (KTD) amongst all the considered algorithms with quadratic time complexity. Under the assumption that the best output list should be Pareto Optimal on the KTD metric for sub-models, we show that our RA algorithm has higher efficiency and coverage in exploring the optimal weights. Combined with the idea of Bayesian Optimization and gradient descent, we solve the online contextual Black-Box Optimization task that finds the optimal weights for sub-models given a chosen RA model. RA-EGO has been deployed in our online system and has improved the revenue significantly.
翻译:电子商业的集合模型结合了多个子模型的预测,用于排名和收入改善。工业混合模型通常是深神经网络,遵循受监督的学习范式,以推算子模型投入的转换率。然而,这一过程有以下两个问题。首先,点入式评分方法忽视了项目之间的关系,导致显示结果一致,同时多样化地展示了用户的经验和收入。第二,学习范式侧重于评级衡量标准,而不是直接优化收入。在我们的工作中,我们提出了一个新的“学习到添加(LTE)框架 RAEGO ”, 以监督的学习模式取代了从子模型的投入转换率。然而,这一过程有以下两个问题:首先,点入计评分方法忽视了项目之间的关系,并导致以统一的方式显示最佳的在线绩效。我们所选择的“升级到添加(LET)”(LET) 框架 RAE, 在我们所考虑的BILA值中, 将最佳的排序算算法放在了我们最高级的ARC 上。我们最高级的ARI,在最高级的IM IM IM IM 上,我们最高级的A 的ALA,我们最优化的O 的计算中,在最高级的ALILILILILILILA 和最高级的计算中,在最高级的计算中,我们最高级的排序的计算中,在最高级的排序的计算到最高级的ALIB IM IM IM IM IM IM IM 的计算法在最精度列表中,在我们最精度 的排序的排序的计算中,我们最精到最精度变到最精到最精到最精度的RA IM IM 。