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:排名组合算法是实际推荐系统中的一个关键组件。当用户访问某个平台时,系统会准备多个商品列表,每个列表通常都来自于一个单一行为目标的推荐模型。由于用户的多种行为意图,例如同时点击和购买某个特定类别的商品,往往同时存在于用户的访问中,因此将多个单一目标的排名列表集成到一起是必要的。然而,之前的排名聚合工作主要关注于融合同质化的具有相同目标的商品列表,而忽略了使用不同目标进行排名的异质性列表的集合方法。在本文中,我们将用户的可能行为和潜在互动的商品类别视为用户的意图。我们旨在研究如何针对用户意图融合多个单一目标的候选商品列表。为了解决这个问题,我们提出了一种意图感知的排名组合学习(IntEL)模型,以用户意图为基础,融合了来自不同目标的多个单一目标的商品列表,并学习商品级别的个性化权重。此外,我们通过误差模糊度分解从点、对和列表丢失函数三个角度理论上证明了IntEL模型的有效性。实验结果表明,在两个大规模真实数据集上,与先前的排名组合模型相比,IntEL在多个行为目标上都有显著的提高。