Deep Item-based Collaborative Filtering for Top-N Recommendation 阅读心得
摘要 近年来，随着机器学习的流行，从数据中学习物品的相似性（或表示）已经成为ItenCF的一个重要部分。现有的itemcf的研究方法仅仅考虑了物品间的线性和浅层的关系，不能有效建模用户复杂的决策过程。文章提出了一种更具表现力的itemcf模型，考虑了物品间的非线性和高阶关系。模型通过非线性神经网络考虑所有交互物品对之间的交互作用。这样做的一个好处是模型可以区分用户交互过的所有物品中哪些物品对用户还
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