  Self-supervised learning (SSL) recently has achieved outstanding success on
recommendation. By setting up an auxiliary task (either predictive or
contrastive), SSL can discover supervisory signals from the raw data without
human annotation, which greatly mitigates the problem of sparse user-item
interactions. However, most SSL-based recommendation models rely on
general-purpose auxiliary tasks, e.g., maximizing correspondence between node
representations learned from the original and perturbed interaction graphs,
which are explicitly irrelevant to the recommendation task. Accordingly, the
rich semantics reflected by social relationships and item categories, which lie
in the recommendation data-based heterogeneous graphs, are not fully exploited.
To explore recommendation-specific auxiliary tasks, we first quantitatively
analyze the heterogeneous interaction data and find a strong positive
correlation between the interactions and the number of user-item paths induced
by meta-paths. Based on the finding, we design two auxiliary tasks that are
tightly coupled with the target task (one is predictive and the other one is
contrastive) towards connecting recommendation with the self-supervision
signals hiding in the positive correlation. Finally, a model-agnostic
DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and
recommendation tasks is developed. The extensive experiments conducted on three
real-world datasets demonstrate that DUAL can significantly improve
recommendation, reaching the state-of-the-art performance.
