6.8　本章小结

本章对矩阵分解算法原理、工程实践、应用场景、优缺点等进行了比较全面的总结。矩阵分解算法是真正意义上的基于模型的协同过滤算法。该算法通过将用户和标的物嵌入到低维隐式特征空间，获得用户和标的物的特征向量表示，再通过向量的内积来量化用户对标的物的兴趣偏好，思路非常简单、清晰，也易于工程实现，效果也相当不错，所以在工业界有非常广泛的应用。矩阵分解算法算是开启了嵌入类方法的先河，在NLP领域非常出名的Word2vec也是嵌入方法的代表，深度学习兴起后，各类嵌入方法在大量的业务场景中得到了大规模采用。

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