《Hybrid Recommender System based on Autoencoders》理解
1 原文 2 原文（與1同一個模型） 3 理解 3.1 本文目的 學習用戶和物品的非線性表示，並利用輔助信息來緩解冷啓動問題。在預測過程中提高精度以及模型的魯棒性 3.2 模型 1、模型輸入 R（UxI）+ Side information 用戶物品評分矩陣的某一列（item-based）或者某一行（user-based）加上輔助信息 2、模型輸出 對R矩陣缺失值的預測 3、優化目標 最小化RMS
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相關文章
- 1. Sequential Recommender System based on Hierarchical Attention Network
- 2. paper review : Multimodal data fusion framework based on autoencoders for top-N recommender systems
- 3. A Survey of Recommender Systems Based on Deep Learning （1）
- 4. Deep Learning based Recommender System: A Survey and New Perspectives
- 5. Text Generation based Variational Autoencoders
- 6. 論文解讀：A content-based recommender system for computer science publications
- 7. Deep Learning based Recommender System: A Survey and New Perspectives （2）
- 8. conversational recommender system論文筆記；推薦系統(recommender system)+對話系統(dialogue system)
- 9. Deep Learning based Recommender System: A Survey and New Perspectives （1）
- 10. An Efficient Deep Learning Approach for Collaborative Filtering Recommender System