The invention discloses a zero-sample-oriented cross-domain recommendation method and
system, and belongs to the field of recommendation systems. The method comprises the following steps: pre-training a residual quantization variational auto-
encoder by using a multi-field article
data set, wherein the residual quantization variational auto-
encoder is used for encoding an article into a multi-level concept identifier (ID); during pre-training,
formatted text description needs to be carried out on an article, optimization is carried out based on a curriculum learning method, and entropy penalty loss is introduced; training an autoregressive large
language model by using the cross-domain recommendation
data set, so that the autoregressive large
language model predicts the multi-level concept identifier ID of the next to-be-interacted article according to the multi-level concept identifier ID of the historical interaction article of the user; and sorting the to-be-recommended article
list by adopting the trained model. According to the method, articles are encoded into multi-level concept identifiers (ID), article marks are unified, course learning is adopted to reduce the initial training complexity and relieve the
codebook collapse problem, the
codebook use frequency is dynamically monitored, the knowledge migration efficiency is improved, and the method can be applied to article recommendation tasks in different fields.