  The click behavior is the most widely-used user positive feedback in
recommendation. However, simply considering each click equally in training may
suffer from clickbaits and title-content mismatching, and thus fail to
precisely capture users' real satisfaction on items. Dwell time could be viewed
as a high-quality quantitative indicator of user preferences on each click,
while existing recommendation models do not fully explore the modeling of dwell
time. In this work, we focus on reweighting clicks with dwell time in
recommendation. Precisely, we first define a new behavior named valid read,
which helps to select high-quality click instances for different users and
items via dwell time. Next, we propose a normalized dwell time function to
reweight click signals in training for recommendation. The Click reweighting
model achieves significant improvements on both offline and online evaluations
in real-world systems.
