DNN论文分享 - Item2vec: Neural Item Embedding for Collaborative Filtering
本篇文章在 ICML2016 Machine Learning for Music Discovery Workshop 前置点评： 这篇文章比较朴素，创新性不高，基本是参照了google的word2vec方法，应用到推荐场景的i2i相似度计算中，但实际效果看还有有提升的。主要做法是把item视为word，用户的行为序列视为一个集合，item间的共现为正样本，并按照item的频率分布进行负样本采样
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相关文章
- 1. DNN论文分享 - Item2vec: Neural Item Embedding for Collaborative Filtering
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