The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations. Despite their significance, data scarcity issues have significantly impaired the effectiveness of existing RS models and hindered their progress. To address this challenge, the concept of knowledge transfer, particularly from external sources like pre-trained language models, emerges as a potential solution to alleviate data scarcity and enhance RS development. However, the practice of knowledge transfer in RSs is intricate. Transferring knowledge between domains introduces data disparities, and the application of knowledge transfer in complex RS scenarios can yield negative consequences if not carefully designed. Therefore, this article contributes to this discourse by addressing the implications of data scarcity on RSs and introducing various strategies, such as data augmentation, self-supervised learning, transfer learning, broad learning, and knowledge graph utilization, to mitigate this challenge. Furthermore, it delves into the challenges and future direction within the RS domain, offering insights that are poised to facilitate the development and implementation of robust RSs, particularly when confronted with data scarcity. We aim to provide valuable guidance and inspiration for researchers and practitioners, ultimately driving advancements in the field of RS.

翻译：在线内容的普及导致推荐系统(RS)得到广泛应用，服务于新闻、广告和电子商务推荐等多种目的。尽管推荐系统具有重要意义，但数据稀缺性问题严重削弱了现有RS模型的效能，并阻碍了其发展。为应对这一挑战，知识迁移概念（特别是从预训练语言模型等外部来源）成为缓解数据稀缺性、促进RS发展的一种潜在解决方案。然而，RS中的知识迁移实践十分复杂。跨域迁移知识会引发数据差异，若设计不当，在复杂RS场景中应用知识迁移可能带来负面后果。因此，本文通过阐述数据稀缺性对RS的影响，并引入数据增强、自监督学习、迁移学习、宽度学习和知识图谱利用等多种策略来缓解这一挑战，为该领域研究做出贡献。此外，文章深入探讨了RS领域的挑战与未来方向，为在面临数据稀缺性时开发和实施稳健的RS提供了洞见。我们旨在为研究人员和实践者提供有价值的指导与启示，最终推动RS领域的进步。