@Item2Vec: Neural Item Embedding for Collaborative Filtering
[[Abstract]]
Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities.
Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as [[word2vec]], was shown to provide state-of-the-art results on various linguistics tasks.
In this paper, we show that item-based CF can be cast in the same framework of neural word embedding.
Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space.
The method is capable of inferring item-item relations even when user information is not available.
We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.
如何定义正样本。#card
Item2Vec认为对于被同一个用户在同一个会话交互过的物料，彼此应该是相似的，它们的向量应该是相近的。
但是考虑到，如果让一个序列内部的物料两两组合，生成的正样本太多了。
因此Item2Vec照搬Word2Vec，也采用滑窗，即只在某个物料前后出现的其他物料才被认为彼此相似，成为正样本。
如何定义负样本。#card
- 照搬Word2Vec，从整个物料库中随机采样一部分物料，与当前物料组合成负样本。
如何 embedding #card
如何定义损失函数#card
- word2vec neg loss
@Item2Vec: Neural Item Embedding for Collaborative Filtering