  In modern advertising and recommender systems, multi-task learning (MTL)
paradigm has been widely employed to jointly predict diverse user feedbacks
(e.g. click and purchase). While, existing MTL approaches are either rigid to
adapt to different scenarios, or only capture coarse-grained task relatedness,
thus making it difficult to effectively transfer knowledge across tasks.
  To address these issues, in this paper, we propose an Adaptive Fine-grained
Task Relatedness modeling approach, AdaFTR, for joint CTR-CVR estimation. Our
approach is developed based on a parameter-sharing MTL architecture, and
introduces a novel adaptive inter-task representation alignment method based on
contrastive learning.Given an instance, the inter-task representations of the
same instance are considered as positive, while the representations of another
random instance are considered as negative. Furthermore, we explicitly model
fine-grained task relatedness as the contrast strength (i.e. the temperature
coefficient in InfoNCE loss) at the instance level. For this purpose, we build
a relatedness prediction network, so that it can predict the contrast strength
for inter-task representations of an instance. In this way, we can adaptively
set the temperature for contrastive learning in a fine-grained way (i.e.
instance level), so as to better capture task relatedness. Both offline
evaluation with public e-commerce datasets and online test in a real
advertising system at Alibaba have demonstrated the effectiveness of our
approach.
