论文标题
分解和相似性混合的有效性
The effectiveness of factorization and similarity blending
论文作者
论文摘要
协作过滤（CF）是一种广泛使用的技术，它允许利用过去用户的偏好数据来识别行为模式并利用它们来预测自定义建议。在这项工作中，我们说明了在苏黎世EthZürich的计算智能实验室（CIL）项目的背景下对不同CF技术的评论。在评估了各个模型的性能之后，我们表明基于混合分解的方法和基于相似性的方法可能导致表现最佳的独立模型显着降低（-9.4％）。此外，我们提出了一种新型的随机扩展，即SCSR的相似性模型，该模型始终降低原始算法的渐近复杂性。
Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Zürich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.
