Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's preferences from the source domain to the target domain, is the key issue in Cross-domain Recommendation (CDR) which is a promising solution to deal with the cold-start problem. Most existing methods model a common preference bridge to transfer preferences for all users. Intuitively, since preferences vary from user to user, the preference bridges of different users should be different. Along this line, we propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR). Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user. To learn the meta network stably, we employ a task-oriented optimization procedure. With the meta-generated personalized bridge function, the user's preference embedding in the source domain can be transformed into the target domain, and the transformed user preference embedding can be utilized as the initial embedding for the cold-start user in the target domain. Using large real-world datasets, we conduct extensive experiments to evaluate the effectiveness of PTUPCDR on both cold-start and warm-start stages. The code has been available at \url{https://github.com/easezyc/WSDM2022-PTUPCDR}.

翻译：冷启动问题仍然是推荐者系统中一个极具挑战性的问题。 幸运的是, 冷启动用户在辅助源域中的互动可以帮助在目标域中提出冷启动建议。 如何将用户的偏好从源域转移到目标域。 如何将用户的偏好从源域转移到目标域, 是跨域建议( CDR) 中的一个关键问题, 这是解决冷启动问题的一个很有希望的解决办法。 大多数现有方法都建起一个共同的偏好桥, 为所有用户传输偏好。 自然地, 由于用户偏好不同, 不同用户的偏好桥梁应该不同。 沿着这条线, 我们提议了一个名为“ 个人化” 的“ 个人化” 框架, 将用户偏爱转换为“ 个人化” 的“ 个人化”, 将用户首选项转换为“ 用户偏好”, 跨域建议( PTUPUPCDDDD) 。 具体地, 将用户的特有的连接连接功能转换为“ 个人化 ”, 将用户端域域中的用户端点, 将“ 用于用户端数据库/ 的首机/ DRDRDRDUDR, 的首部 。 在用户的首机 的首机 。