个性化推荐是帮助人们在信息爆炸的时代快速获取所需信息的一种重要途径。推荐系统的数据稀疏性是影响推荐算法实际应用中准确性的重要因素，主要涉及冷启动用户、长尾用户、稀疏特征和稀疏交互。因此，本文针对稀疏数据下的推荐方法进行研究，使得推荐算法能够在稀疏数据下保持良好推荐效果。 首先，新用户刚进入系统时，是没有交互历史的冷启动用户。本文提出了针对推荐的离线自动化词网络构建，不仅针对推荐设计了词之间的三种关系，且覆盖了大量词语和连边。通过图神经网络建模词网络，借助跨领域交互和内容增强文本表示，所提出方法在离线排序推荐、点击预测和在线召回实验上较基线方法可提升5\%以上。其次，新用户产生交互后进入低频长尾用户阶段，交互少却占用大量计算资源。本文提出了高效的自适应用户偏好聚合，用少量偏好向量表示所有用户的偏好，每个用户不再由一个向量存储偏好。该方法不仅节省了模型参数和存储空间，还显式让长尾用户借鉴高频用户交互历史，并在用户有更多交互后自适应调整其偏好表征。在多个数据集上的实验结果表明，该方法可以提升多个推荐模型在长尾用户上的推荐效果达1\%以上，同时节省70\%以上模型参数。然后，即使是一般用户，数据中各类特征也存在稀疏缺失情况。本文提出了考虑特征重要性的自适应特征采样训练方法，在训练过程中检测模型对不同特征的依赖程度，自适应引入复杂缺失特征样本帮助模型学习如何在重要特征稀疏情况下进行预测。实验结果表明该方法显著提升了多种模型在稀疏特征下的推荐效果。最后，即使是高频用户和商品，整个用户商品交互矩阵中已观测到的数据仍然是十分稀疏的。为了提升模型对稀疏交互特征的学习效率，本文将逻辑推理引入神经网络中，利用所设计的自监督任务赋予了神经网络模块相应的逻辑和数学运算性质，不仅能够进行形式化的逻辑表达式推理和用户行为推理，还进一步拓展到基于特征的用户建模任务。该方法在取得与最先进方法相当的用户建模效果同时，能够给出逻辑推理规则作为输出依据，并显著提升在稀疏交互数据下的效果。 本文提出的方案涉及模型训练、内容表征、偏好建模、行为推理等多个方面，全面系统地在用户各个阶段提升推荐模型在稀疏数据下的效果，不仅具有重要的研究意义，还具有广阔的应用前景。

Personalized recommendation is an important way to help people efficiently achieve their required information in the era of information explosion. Data sparsity, which includes cold-start users, long-tail users, sparse features, and sparse interactions, is one of the most important factors that affect recommendation accuracy in practical applications. As a result, this paper investigates the recommendation approach for sparse data, with the goal of improving the performance of recommendation algorithms when dealing with sparse data. First of all, when a new user comes to the system, he is a cold-start user without interaction history. This paper proposes an offline method of automated word graph construction for the recommendation that not only designs three word relationships but also covers a large number of words and edges. With the help of cross-domain interaction and content, modeling word networks with graph neural networks can improve cross-domain text representation. In offline ranking recommendation, click prediction, and online recall experiments, the proposed method outperforms the baseline method by over 5\%. Secondly, after the new user interacts with some items, he becomes a low-frequency long-tail user. Second, new users become low-frequency long-tail users after interacting with a few items. They have few interactions, but they consume a lot of computing power. This paper proposes a novel user preference aggregation strategy in which all users' preferences are represented by a small number of preference vectors rather than a single vector for each user. Not only does this method save model parameters, but it also allows long-tail users to learn from the history of high-frequency user interactions. As users interact, their preference representations are updated adaptively. Experiments on multiple datasets show that this method can improve the recommendation effect of multiple recommendation models on long-tail users by over 1\% while saving over 70\% of model parameters. Then, even for normal users, various features in the data may be sparse and have missing values. This paper proposes an adaptive feature sampling training method that takes feature importance into account. It detects the model's dependence on different features during the training process, and introduces complex missing feature samples adaptively to help the model learn how to predict when important features are sparse. Experimental results show that the feature sampling strategy significantly improves the recommendation performance of various models under sparse features. Finally, even for high-frequency users and items, the observed data in the entire user-item interaction matrix is still very sparse. To improve the learning efficiency of the model on sparse interaction features, this paper introduces logical reasoning into the neural network and designs self-supervised tasks to empower neural modules with properties of logical and mathematical operations. The proposed method can conduct formal logic expression reasoning and user behavior reasoning and is further extended to feature-based user modeling tasks. This method achieves comparable user modeling performance to state-of-the-art methods. At the same time, it provides logical rules as explanations of outputs and significantly improves the performance under sparse interaction data. The proposed methods in this paper involve model training, content representation, preference modeling, behavior reasoning, etc. It comprehensively and systematically improves the performance of the recommendation model under sparse data at all stages of a user, which has broad application prospects and vital research significance.