基于跨域元学习的用户冷启动推荐算法OA

To address two issues in existing mapping-based cross-domain recommendation algorithms—namely,the neglect of target domain contextual information during interest transfer and the model's high sensitivity to the scale of overlapping users—a novel end-to-end optimized Cross-domain Meta-learning Driven Interest Transfer Network(CMITN)is proposed.CMITN introduces a cross-domain attention mechanism that is sensitive to the target domain,which reconstructs the source domain's interest represen-taion based on candidate item characteristics.A convolutional meta-learning network is designed to dynamically generate personal-ized mapping functions,alleviating data sparsity dependence.A dual-objective joint optimization paradigm is constructed to opti-mize both the recommendation task and the representation alignment,ensuring a consistent mapping of the full interest space.Five cross-domain recommendation tasks were constructed using Amazon and Hello datasets,followed by extensive experiments.The ex-perimental results show that the proposed algorithm outperforms other baseline models in both AUC(Area Under Curve)and Recall.In the business scenario of Hello Life Services,the algorithm led to a 4.5%increase in CTR(Click Through Rate)and a 7.0%im-provement in GMV(Gross Merchandise Volume)per user for new users.