  A mixed list of ads and organic items is usually displayed in feed and how to
allocate the limited slots to maximize the overall revenue is a key problem.
Meanwhile, modeling user preference with historical behavior is essential in
recommendation and advertising (e.g., CTR prediction and ads allocation). Most
previous works for user behavior modeling only model user's historical
point-level positive feedback (i.e., click), which neglect the page-level
information of feedback and other types of feedback. To this end, we propose
Deep Page-level Interest Network (DPIN) to model the page-level user preference
and exploit multiple types of feedback. Specifically, we introduce four
different types of page-level feedback as input, and capture user preference
for item arrangement under different receptive fields through the multi-channel
interaction module. Through extensive offline and online experiments on Meituan
food delivery platform, we demonstrate that DPIN can effectively model the
page-level user preference and increase the revenue for the platform.
