  There has been many studies on improving the efficiency of shared learning in
Multi-Task Learning(MTL). Previous work focused on the "micro" sharing
perspective for a small number of tasks, while in Recommender Systems(RS) and
other AI applications, there are often demands to model a large number of tasks
with multi-dimensional task relations. For example, when using MTL to model
various user behaviors in RS, if we differentiate new users and new items from
old ones, there will be a cartesian product style increase of tasks with
multi-dimensional relations. This work studies the "macro" perspective of
shared learning network design and proposes a Multi-Faceted Hierarchical MTL
model(MFH). MFH exploits the multi-dimension task relations with a nested
hierarchical tree structure which maximizes the shared learning. We evaluate
MFH and SOTA models in a large industry video platform of 10 billion samples
and results show that MFH outperforms SOTA MTL models significantly in both
offline and online evaluations across all user groups, especially remarkable
for new users with an online increase of 9.1\% in app time per user and 1.85\%
in next-day retention rate. MFH now has been deployed in a large scale online
video recommender system. MFH is especially beneficial to the cold-start
problems in RS where new users and new items often suffer from a "local
overfitting" phenomenon. However, the idea is actually generic and widely
applicable to other MTL scenarios.
