Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the \(({ CF})^2\) architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.

中文翻译：

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（CF）2体系结构：上下文协作过滤

推荐系统极大地改变了我们消费内容的方式。Internet应用程序依靠这些系统来帮助用户在越来越多的可用选择中进行导航。但是，大多数当前系统都忽略了这样的事实，即用户首选项可以根据上下文进行更改，从而导致不符合用户兴趣的推荐。本研究通过提出\（（{CF}）^ 2 \）解决了这些问题。架构，该架构使用本地学习技术将上下文意识嵌入协作过滤模型中。在两个分别涉及超过1.3亿和700万个独特样本的大规模案例研究中论证了所提议的体系结构。结果表明，使用少量数据训练的上下文模型提供了与使用完整数据集训练的协作过滤模型相似的准确性。此外，与随机选择模型相比，基于上下文的模型的准确性更高，已经证明了在现实世界数据集中考虑上下文的​​影响。