  Integrated recommendation, which aims at jointly recommending heterogeneous
items from different channels in a main feed, has been widely applied to
various online platforms. Though attractive, integrated recommendation requires
the ranking methods to migrate from conventional user-item models to the new
user-channel-item paradigm in order to better capture users' preferences on
both item and channel levels. Moreover, practical feed recommendation systems
usually impose exposure constraints on different channels to ensure user
experience. This leads to greater difficulty in the joint ranking of
heterogeneous items. In this paper, we investigate the integrated
recommendation task with exposure constraints in practical recommender systems.
Our contribution is forth-fold. First, we formulate this task as a binary
online linear programming problem and propose a two-layer framework named
Multi-channel Integrated Recommendation with Exposure Constraints (MIREC) to
obtain the optimal solution. Second, we propose an efficient online allocation
algorithm to determine the optimal exposure assignment of different channels
from a global view of all user requests over the entire time horizon. We prove
that this algorithm reaches the optimal point under a regret bound of $
\mathcal{O}(\sqrt{T}) $ with linear complexity. Third, we propose a series of
collaborative models to determine the optimal layout of heterogeneous items at
each user request. The joint modeling of user interests, cross-channel
correlation, and page context in our models aligns more with the browsing
nature of feed products than existing models. Finally, we conduct extensive
experiments on both offline datasets and online A/B tests to verify the
effectiveness of MIREC. The proposed framework has now been implemented on the
homepage of Taobao to serve the main traffic.
