maxMF-Nonlinear Latent Factorization by Embedding Multiple User Interests
1：经典的推荐会采用矩阵分解方式来学习用户、商品的潜在向量，并用用户与商品向量的相似度来召回商品。 2：什么是maxMF？ 这里的maxMF区别去ALS是，它不像ALS只会有一个向量，他的前提假设是一个用户更加复杂，我们不能靠一个单纯的向量去表示用户，我们需要多个向量去表示用户，然后用一个向量去表示商品。这样我们用户与商品匹配得分就是取用户多个向量里那个最大得分作为匹配得分。而且这个模型是一个非线
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相关文章
- 1. Discovering Urban Functional Zones By Latent Fusion of Users GPS Data and Point of Interests
- 2. Hamming Embedding, Burstiness, Multiple Assignment
- 3. 文论瞎读：Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
- 4. Word Embedding
- 5. ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face
- 6. Nonlinear Dimensionality Reduction by Locally Linear Embedding
- 7. MF系列一：从Matrix Factorization到Probabilistic Matrix Factorization
- 8. ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
- 9. Controllable Multi-Interest Framework for Recommendation
- 10. select files by multiple extension name