  With the proliferation of short video applications, the significance of short
video recommendations has vastly increased. Unlike other recommendation
scenarios, short video recommendation systems heavily rely on feedback from
watch time. Existing approaches simply treat watch time as a direct label,
failing to effectively harness its extensive semantics and introduce bias,
thereby limiting the potential for modeling user interests based on watch time.
To overcome this challenge, we propose a framework named Debiased
Multiple-semantics-extracting Labeling(DML). DML constructs labels that
encompass various semantics by utilizing quantiles derived from the
distribution of watch time, prioritizing relative order rather than absolute
label values. This approach facilitates easier model learning while aligning
with the ranking objective of recommendations. Furthermore, we introduce a
method inspired by causal adjustment to refine label definitions, thereby
directly mitigating bias at the label level. We substantiate the effectiveness
of our DML framework through both online and offline experiments. Extensive
results demonstrate that our DML could effectively leverage watch time to
discover users' real interests, enhancing their engagement in our application.
