  Multi-domain recommendation and multi-task recommendation have demonstrated
their effectiveness in leveraging common information from different domains and
objectives for comprehensive user modeling. Nonetheless, the practical
recommendation usually faces multiple domains and tasks simultaneously, which
cannot be well-addressed by current methods. To this end, we introduce M3oE, an
adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework.
M3oE integrates multi-domain information, maps knowledge across domains and
tasks, and optimizes multiple objectives. We leverage three mixture-of-experts
modules to learn common, domain-aspect, and task-aspect user preferences
respectively to address the complex dependencies among multiple domains and
tasks in a disentangled manner. Additionally, we design a two-level fusion
mechanism for precise control over feature extraction and fusion across diverse
domains and tasks. The framework's adaptability is further enhanced by applying
AutoML technique, which allows dynamic structure optimization. To the best of
the authors' knowledge, our M3oE is the first effort to solve multi-domain
multi-task recommendation self-adaptively. Extensive experiments on two
benchmark datasets against diverse baselines demonstrate M3oE's superior
performance. The implementation code is available to ensure reproducibility.
