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首页> 外文会议>2nd KDD workshop on large-scale recommender systems and the netflix prize competition 2008 >Improved Neighborhood-Based Algorithms for Large-Scale Recommender Systems
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Improved Neighborhood-Based Algorithms for Large-Scale Recommender Systems

机译：大型推荐系统的改进的基于邻域的算法

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摘要

Neighborhood-based algorithms are frequently used modules of recommender systems. Usually, the choice of the similarity measure used for evaluation of neighborhood relationships is crucial for the success of such approaches. In this article we propose a way to calculate similarities by formulating a regression problem which enables us to extract the similarities from the data in a problem-specific way. Another popular approach for recommender systems is regularized matrix factorization (RMF). We present an algorithm -neighborhood-aware matrix factorization - which efficiently includes neighborhood information in a RMF model. This leads to increased prediction accuracy. The proposed methods are tested on the Netflix dataset.
机译：基于邻域的算法是推荐系统的常用模块。通常，选择用于评估邻居关系的相似性度量对于此类方法的成功至关重要。在本文中，我们提出了一种通过公式化回归问题来计算相似度的方法，该回归问题使我们能够以问题特定的方式从数据中提取相似度。推荐系统的另一种流行方法是正则化矩阵分解（RMF）。我们提出了一种算法-邻域感知矩阵分解-该算法将邻域信息有效地包含在RMF模型中。这导致增加的预测准确性。提议的方法在Netflix数据集上进行了测试。

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