Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings.

We conducted the experiment on Meta-World, a multi-task RL benchmark containing various robotics manipulation tasks.

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Even in large language models, negative transfer problems may still exist.

Even in large language models, negative transfer problems may still exist.

Tocircumvent thisissue,wepropose anovelmulti-task learning method, whichsimultaneously learns latent task representations and a block-diagonal Latent Task Assignment Matrix (LTAM).

Continual learning aims to learn a sequence of tasks from dynamic data distributions.