EAGER: Collaborative Research: Cross-Domain Knowledge Transformation via Matrix Decompositions

EAGER：合作研究：基于矩阵分解的跨领域知识转换传统的数据挖掘算法从零开始发现新领域的知识，忽略了在其他领域学习到的知识。 知识转换是一种转换范式，它利用先前在其他领域获得的知识来指导新领域的知识发现过程，特别适用于大型数据集。 特别是，利用其他领域的适用知识有助于稳定无监督学习，并生成我们可能有初步了解的结果。 本计画的目标是设计与发展跨领域知识转换机制，以促进知识发现。转换机制是基于矩阵分解的知识被转移的直接和明确的表示？使它们易于理解并在实践中使用。所提出的机制提供了一个通用的知识转换框架，具有坚实的理论基础，使一个新的范式的无监督学习领域知识。这些知识转换机制/系统的有用性将被证明为有效的信息检索，消费者推荐系统，和产品/在线意见情感分析。这种变革方法的多功能性将在许多领域得到验证。

EAGER: Collaborative Research: Cross-domain Knowledge Transformation via Matrix DecompositionsTraditional data mining algorithms discover knowledge in new domains starting from the scratch, ignoring knowledge learned in other domains. Knowledge transformation is a transformative paradigm that utilizes previously acquired knowledge in other domains to guide knowledge discovery process in a new domain and is especially useful for large data sets. In particular, utilizing applicable knowledge in other domains helps to stabilize the unsupervised learning and generate results that we may have preliminary understanding. The goal of this project is to design and develop cross-domain knowledge transformation mechanisms for knowledge discovery. The transformation mechanisms are based on matrix decompositions where the knowledge been transferred are represented directly and explicitly ? making them easy to comprehend and be utilized in practice. The proposed mechanisms provide a versatile knowledge transformation framework with solid theoretical foundation and enable a new paradigm of unsupervised learning with domain knowledge. The usefulness of these knowledge transformation mechanisms/systems will be demonstrated for effective information retrieval, consumer recommender systems, and product/online opinion sentiment analysis. The versatility of this transformative metholody will be verified across many domains.