maxMF-Nonlinear Latent Factorization by Embedding Multiple User Interests
1：經典的推薦會採用矩陣分解方式來學習用戶、商品的潛在向量，並用用戶與商品向量的相似度來召回商品。 2：什麼是maxMF？ 這裏的maxMF區別去ALS是，它不像ALS只會有一個向量，他的前提假設是一個用戶更加複雜，我們不能靠一個單純的向量去表示用戶，我們需要多個向量去表示用戶，然後用一個向量去表示商品。這樣我們用戶與商品匹配得分就是取用戶多個向量裏那個最大得分作爲匹配得分。而且這個模型是一個非線
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相關文章
- 1. Discovering Urban Functional Zones By Latent Fusion of Users GPS Data and Point of Interests
- 2. Hamming Embedding, Burstiness, Multiple Assignment
- 3. 文論瞎讀：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：從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