Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents. In this paper, we treat a user's possible behaviors and the potential interacting item categories as the user's intent. And we aim to study how to fuse candidate item lists generated from different objectives aware of user intents. To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned. Furthermore, we theoretically prove the effectiveness of IntEL with point-wise, pair-wise, and list-wise loss functions via error-ambiguity decomposition. Experiments on two large-scale real-world datasets also show significant improvements of IntEL on multiple behavior objectives simultaneously compared to previous ranking ensemble models.

翻译：排序集成是真实推荐系统中的关键组件。当用户访问平台时，系统会准备多个物品列表，每个列表通常来自单一行为目标的推荐模型。由于用户访问时多种行为意图（例如点击和购买特定物品类别）通常同时存在，有必要将多个单目标排序列表整合为一个列表。然而，以往的排序聚合研究主要集中于融合具有相同目标的同质物品列表，而忽略了融合不同用户意图下以不同目标排序的异质列表。在本文中，我们将用户的可能行为及潜在交互的物品类别视为用户的意图，并旨在研究如何融合从不同目标生成的候选物品列表，以感知用户意图。为解决此任务，我们提出了一种意图感知的排序集成学习模型（IntEL），用于融合多个具有不同用户意图的单目标物品列表，其中学习了物品级别的个性化权重。此外，我们通过误差-歧义分解，理论证明了IntEL在点级、对级和列表级损失函数下的有效性。在两个大规模真实数据集上的实验也表明，与以往的排序集成模型相比，IntEL在多个行为目标上同时取得了显著提升。