Deep Item-based Collaborative Filtering for Top-N Recommendation 閱讀心得
摘要 近年來，隨着機器學習的流行，從數據中學習物品的相似性（或表示）已經成爲ItenCF的一個重要部分。現有的itemcf的研究方法僅僅考慮了物品間的線性和淺層的關係，不能有效建模用戶複雜的決策過程。文章提出了一種更具表現力的itemcf模型，考慮了物品間的非線性和高階關係。模型通過非線性神經網絡考慮所有交互物品對之間的交互作用。這樣做的一個好處是模型可以區分用戶交互過的所有物品中哪些物品對用戶還
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相關文章
- 1. 閱讀筆記：Item-based Collaborative Filtering Recommendation Algorithms
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