  Neural-based multi-task learning (MTL) has gained significant improvement,
and it has been successfully applied to recommendation system (RS). Recent deep
MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based
parameter-sharing networks that implicitly learn a generalized representation
for each task. However, MTL methods may suffer from performance degeneration
when dealing with conflicting tasks, as negative transfer effects can occur on
the task-shared bottom representation. This can result in a reduced capacity
for MTL methods to capture task-specific characteristics, ultimately impeding
their effectiveness and hindering the ability to generalize well on all tasks.
In this paper, we focus on the bottom representation learning of MTL in RS and
propose the Deep Task-specific Bottom Representation Network (DTRN) to
alleviate the negative transfer problem. DTRN obtains task-specific bottom
representation explicitly by making each task have its own representation
learning network in the bottom representation modeling stage. Specifically, it
extracts the user's interests from multiple types of behavior sequences for
each task through the parameter-efficient hypernetwork. To further obtain the
dedicated representation for each task, DTRN refines the representation of each
feature by employing a SENet-like network for each task. The two proposed
modules can achieve the purpose of getting task-specific bottom representation
to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible
to combine with existing MTL methods. Experiments on one public dataset and one
industrial dataset demonstrate the effectiveness of the proposed DTRN.
