  In the video recommendation, watch time is commonly adopted as an indicator
of user interest. However, watch time is not only influenced by the matching of
users' interests but also by other factors, such as duration bias and noisy
watching. Duration bias refers to the tendency for users to spend more time on
videos with longer durations, regardless of their actual interest level. Noisy
watching, on the other hand, describes users taking time to determine whether
they like a video or not, which can result in users spending time watching
videos they do not like. Consequently, the existence of duration bias and noisy
watching make watch time an inadequate label for indicating user interest.
Furthermore, current methods primarily address duration bias and ignore the
impact of noisy watching, which may limit their effectiveness in uncovering
user interest from watch time. In this study, we first analyze the generation
mechanism of users' watch time from a unified causal viewpoint. Specifically,
we considered the watch time as a mixture of the user's actual interest level,
the duration-biased watch time, and the noisy watch time. To mitigate both the
duration bias and noisy watching, we propose Debiased and Denoised watch time
Correction (D$^2$Co), which can be divided into two steps: First, we employ a
duration-wise Gaussian Mixture Model plus frequency-weighted moving average for
estimating the bias and noise terms; then we utilize a sensitivity-controlled
correction function to separate the user interest from the watch time, which is
robust to the estimation error of bias and noise terms. The experiments on two
public video recommendation datasets and online A/B testing indicate the
effectiveness of the proposed method.
