在移动互联网和网络信息量迅速增长的背景下，信息过载成为一个日益突出的问题。推荐系统作为解决方案之一，通过从海量信息中筛选出用户感兴趣的内容，显著提升了用户体验。不过，传统推荐系统依赖于大量的用户-物品互动数据，对于那些交互较少的非活跃用户和物品，推荐性能常常不理想。特别是在缺乏足够互动数据的情况下，系统难以准确捕获冷启动用户的兴趣或物品的特性，因此，为了推荐的个性化和公平性，迫切需要研发创新策略来有效解决冷启动问题。

随着元学习方法的深入研究，现有的研究结果证明了其在少量样本学习中的有效性，并可以应用于缓解推荐系统的冷启动问题。尽管现有方法在一定程度上缓解了这一问题，但对系统中现有的可信数据的利用却有限，仍有部分有价值的数据未被利用。基于此，本文针对基于元学习方法的冷启动问题进行了研究，提出了两种信息融合的方法并在大量数据上进行了实验。本文的研究内容主要分为以下几个方面：

第一，提出基于域内相似物品信息融合的物品冷启动推荐方法（Similarity Based Meta-Learning ID Embedding Generator，SME）。针对现有方法仅关注了物品的ID特征以外的属性信息，对系统内的历史交互数据利用不充分的问题，本文对系统内的数据进行分析，创新性的融合相似物品ID特征融合的方法，通过利用物品自身属性特征以及与其相似物品的ID特征结合，为信息不足的冷门物品生成更加符合其个性特征的ID初始嵌入，最终提高其推荐系统中的预测准确性。

第二，提出基于跨域信息融合的用户冷启动推荐模型（Cross-domain Meta-Learning ID Embedding Generator，CME）。现实场景中，用户通常在不同平台中表现出相类似的偏好取向。由此，可以考虑通过跨域信息的利用，由源域的丰富用户信息来帮助解决目标域的信息量不足的问题。现有的方法更加注重于两个域的映射关系，对于数据的对应性有一定的要求。本文引入源域用户ID特征来指导目标域ID嵌入生成器的ID嵌入生成。此方法减少了对数据对应性的依赖，利用源域的丰富用户信息辅助目标域的用户ID嵌入生成，从而提升推荐系统的预测效果。

第三，本文在真实世界数据集上进行了充分的实验验证，包括与现有方法的对比实验和模块的消融实验。实验结果分析表明，本文提出的方法相较于其他现有技术，能够有效地缓解推荐系统中的冷启动问题，具有一定的有效性与鲁棒性。

With the mobile internet expanding rapidly and online information ballooning, dealing with too much information has become a significant challenge. Recommendation systems aim to ease this by sorting through the vast amount of content to find what users like, which improves their online experience. However, these systems depend on a lot of data about user-item interactions, and they often fall short for new or less popular items and users with sparse interaction histories. Struggling to pinpoint the preferences of new users or the distinctive features of new items, these systems face the "cold start" problem. Finding innovative solutions to enhance the personalization and fairness of recommendations for these cases is crucial.

With more research into meta-learning methods, these approaches have been shown to work well when there is only a little bit of data. They can help with the cold start problem in recommendation systems. Even though they've made some progress, they still don't use all of the good data that's already in the system. Some valuable information isn't being used at all. This paper looks at the cold start problem through meta-learning methods. We suggest two ways of combining information and test them on big data sets. The research in this paper covers several points:

First, we suggest a new way to recommend items to new users by combining information that's similar, called SME. Current methods don't use all the data that's already there, like the history of what items users liked. Our paper looks at the data we have and puts together features from items that are alike. By using what we know about an item's features and what's similar in other items, we can create ID embeddings that match the unique qualities of items that don't have much information. This should make our recommendations better.

Second, we've created a new model for recommending things to new users across different areas, called CME. Users often like similar things on different platforms. We can use information from one area to help us with another area that doesn't have enough information. Other methods care a lot about how information matches up between areas. Our paper uses user ID features from one area to help make user ID embeddings in another area. This way, we don't need the information to match up exactly. We use rich user information from one area to make better recommendations in another area.

Third, in this study, comprehensive experimental validation was conducted on real-world datasets, which included comparative experiments with existing methods and ablation studies on different modules. The analysis of the experimental results demonstrates that the method proposed in this paper effectively mitigates the cold-start problem in recommendation systems, exhibiting a certain degree of efficacy and robustness compared to other existing technologies.