基于矩阵分解的矩阵分解
Feature-Based Matrix Factorization
摘要：
推荐系统是近年来发展起来的一个研究热点，在许多领域得到了广泛的应用。随着信息量和信息类型的不断增加，如何充分利用这些丰富的信息来提高推荐系统的性能成为推荐系统面临的一大挑战。大多数传统的方法试图为每个场景设计一个特定的模型，这需要在开发和修改模型的巨大努力。在本技术报告中，我们描述了基于特征的矩阵分解的实现。该模型是矩阵分解模型的许多变体的抽象，并且可以通过简单地定义新特性来利用新类型的信息，而无需修改任何代码行。使用该工具包，我们构建了KDDCup'11第1轨报告的最佳单个模型。
英文摘要：
Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
