在大数据背景下，干扰信息和恶意用户大量存在，用户间的信任对在线购物决策起到重要作用。许多大型电商平台为方便用户购物，往往按产品类别来组织产品，形成一套完整的产品层级体系。在此体系下，该课题基于用户的在线评论，研究对应于产品层级的用户分层信任网络的构建及其推荐问题。首先对用户之间的信任关系的建立机制进行研究，基于此提出用户间信任关系推理算法。然后基于评论挖掘，分别构建产品和用户的信息属性模型。在此基础上，针对电商平台的产品层级体系，考虑到用户间信任关系对产品类的情景依赖性，研究由底层到顶层逐层递进的分层信任网络的构建策略。基于分层信任网络，研究推荐策略，实现满足用户多样化需求的个性化推荐。最后进行课题方法的评价与应用研究。通过课题研究，在理论上，力图完善信任网络以及信任推荐的理论体系；在实践中，面对电商实际存在的产品层级体系，力求为用户提供可靠的、多样化的产品推荐，为其在线购物提供决策支持。

In the context of big data, the web frequently hosts false information and mendacious users. The trust among users becomes particularly important in decision-making of their online shopping. For the convenience of consumers, many large-scale e-commerce websites develop a hierarchical system of their products. Under such circumstances, in this research project, online reviews are used to model the hierarchical trust network and the strategy for diverse recommendations. First, the mechanisms that help develop trust among users are investigated, and algorithms of trust inference are proposed. Moreover, through review mining, the products’ attributes model and users’ profile model are built separately. As trust is context-specific to the product category, the hierarchical trust network is modeled progressively, from the bottom to the top layer of the product hierarchy. Then, a recommender strategy is proposed along the hierarchical trust network step-by-step to satisfy the personalized and diversified needs. Finally, the proposed methodology is evaluated and applied in real e-commerce websites. From a theoretical perspective, this study provides significant input to advance the theory system of the trust network and the trust-based social recommender systems. In practice, considering the real hierarchy system of products in e-commerce websites, this research strives to offer more reliable and diverse recommendations and provide decision-making support for online consumers.