H好菇凉666用万字长文聊一聊 Embedding 技术( 十 )

五、总结针对当前热门的embedding技术，本文系统的总结了能处理各类型数据的embedding方法，如传统基于矩阵分解的方法（如SVD分解）、处理文本的embedding方法（如Word2vec、FastText等）以及处理图数据的embedding方法（如DeepWalk、GraphSAGE等）。在推荐系统中，针对于不同数据类型，可以灵活采用上述方法来实现对数据的抽象表示。如可以基于用户行为，构造item列表，采用基于文本的方法对item进行向量化；也可以通过构建user和item关系图，采用基于图的方法来对user和item进行向量化。在实际过程中，不同的向量化方法得到的embedding结果也会有较大差异，需要根据具体业务需求来选择相应的算法。如要挖掘用户与用户的同质性，可以尝试采用Node2vec；此外，如果需要结合物品或Item的side-info ，可以考虑GraphSAGE算法来对图中节点进行embedding 。跟深度学习炼丹术一样，要熟练掌握各类embedding技术，需要根据具体应用场景不断试错积累经验。最后，要司庆了，祝我们“炼丹人”能快乐搬砖！
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