Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area.

翻译：多任务学习已广泛应用于计算视觉、自然语言处理等领域，并取得了良好性能。近年来，涌现了大量关于多任务学习推荐系统的研究工作，但尚未有文献对这些工作进行全面总结。为填补这一空白，我们提供了关于多任务推荐系统的系统性文献综述，旨在帮助研究人员和实践者快速了解该方向的最新进展。本综述首先介绍基于多任务学习的推荐系统的背景与动机，随后根据多任务学习技术的不同阶段（包括任务关系发现、模型架构和优化策略）提出多任务学习推荐方法的分类体系，最后对该领域的应用及未来有前景的研究方向展开讨论。