  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.
