Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users' behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.
翻译:长期存在的数据宽度和冷启动是建议系统棘手和难以解答的问题。跨域建议是一个域适应框架,已用于利用多个领域的信息,高效率地解决这些具有挑战性的问题。在本研究中,探索了一个项目级相关性跨域建议任务,其中两个相关领域,即源和目标领域包含共同项目,但不分享关于用户行为的敏感信息,从而避免用户隐私的泄露。根据这一设想,为跨域建议提出了两个新颖的、同时结合的基于自动编码器的深层学习方法。第一个方法旨在同时学习一组自动编码器,以揭示来源和目标领域的项目的内在表现,同时同时进行绘图功能,以模拟这些表述之间的非线性关系,从而将有益的信息从来源转移到目标领域。第二个方法基于一个新的联合正规化优化问题,即使用两个自自动化编码器,以深非线性方式生成用户和项目列的深层学习因素。在同一个时间里,将两个可获取的高级数据定位模型用于对比我们所建的高级数据库,同时将两个现有数据基数的高级数据库用于对所建数据库进行对比。