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 Z\"urich. 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.
翻译:合作过滤(CF)是一种广泛使用的技术,它能够利用过去用户的偏好数据确定行为模式并利用这些数据预测自定义建议。在这项工作中,我们举例说明了我们在苏黎世州(Eth)的计算情报实验室(CIL)项目背景下对不同的CF技术的审查。在对单个模型的性能进行评估之后,我们发现,混合基于因素的和基于相似性的方法可以导致最佳独立模型上的重大误差减少(9.4%)。此外,我们提议对类似模型(SCSR)进行创新的随机扩展,该模型会不断减少原始算法的简单复杂性。