【摘要】 为解决推荐系统中数据稀疏造成的用户冷启动问题，文中提出了一种基于方面级用户偏好迁移的跨领域推荐算法(Cross-Domain Recommendation via Review Aspect-Level User Preference Transfer, CAUT),设计了基于两阶段生成对抗网络的用户方面级偏好跨领域迁移结构，通过用户历史评论挖掘用户细粒度方面级偏好。CAUT利用预训练源领域编码器参数对目标领域编码器进行参数初始化，在固定源领域编码器参数的同时引入领域鉴别器，以解决源领域与目标领域数据分布差异的问题，进而可以有效利用源领域的丰富数据，缓解目标领域数据稀疏造成的用户冷启动问题。在亚马逊电商平台真实数据集上进行了实验，结果表明，与最新算法相比，CAUT在用户对商品的评分预测均方根误差(RMSE)指标上有明显的提升，说明CAUT可有效缓解用户冷启动问题。更多还原

【Abstract】 In order to solve the user cold-start problem caused by data-sparse in recommender system, this paper proposes a cross-domain recommendation algorithm based on aspect-level user preference transfer, named CAUT.CAUT is devised to learn aspect transfer across domains from a two-stage generative adversarial network and extract aspect-level user fine-grained prefe-rence from reviews.The data distribution misalignment between source and target domains is eliminated by fixing source domain encoder parameters and designing a domain discriminator.Then the user cold-start problem caused by data-sparse in the target domain could be alleviated by utilizing source domain data via CAUT.Experiments on real-world datasets show that the proposed CAUT outperforms SOTA models significantly in rating prediction RMSE indicator, suggesting that CAUT can effectively solve the user cold-start problem.更多还原