Top- N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as follows: (1) with BDR, item clustering is embedded into the learning of ICF methods; (2) BDR induces sparsity of item similarities, which guarantees recommendation efficiency; and (3) BDR captures in-block transitivity to overcome rating sparsity. By regularizing the item similarity matrix of item similarity models with BDR, we obtain a block-aware item similarity model. Our experimental evaluations on a large number of datasets show that the block-diagonal structure is crucial to the performance of top- N recommendation.

中文翻译：

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前 N 推荐的块感知项目相似性模型

最佳-ñ建议已被广泛研究。最近的基于项目的协同过滤（ICF）方法取得了有希望的结果。ICF 的关键在于项目相似性的估计。观察块对角结构对于实践中的项目相似性，我们针对 ICF 的项目相似性提出了块对角正则化 (BDR)。BDR背后的直觉如下：（1）使用BDR，项目聚类嵌入到ICF方法的学习中；（2）BDR导致项目相似度稀疏，保证推荐效率；(3) BDR 捕获块内传递性以克服评级稀疏性。通过使用 BDR 对项目相似度模型的项目相似度矩阵进行正则化，我们得到了一个块感知项目相似度模型。我们对大量数据集的实验评估表明，块对角结构对 top-dictional 的性能至关重要。ñ推荐。