Contexts have been proven to be an important source of information that can significantly improve the performance of collaborative filtering (CF), e.g., for recommendation. Most context-aware approaches that are basing on latent factor models assume that contexts share the same latent space with users and items. However such a strategy does not always make sense, e.g., the influence of contextual information may be overestimated. In this paper, we propose a generic framework to learn context-aware latent representations for context-aware collaborative filtering without imposing contexts into latent space of users and items. Contextual contents are combined via a function to produce the contextual influence factor, which is then combined with each latent factor to derive latent representations. We instantiate the generic framework using biased Matrix Factorization for rating prediction task and Bayesian Personalized Ranking (BPR) for item recommendation tasks. Stochastic Gradient Descent (SGD) based optimization procedures are developed to fit the two context-aware models by jointly learning the weight of each context and latent factors of users and items. Experiments conducted on three real-world datasets demonstrate that our context-aware CF model significantly outperforms not only the base models but also the representative context-aware models.

事实证明，上下文是可以显着提高协作过滤（CF）性能（例如，推荐）的重要信息源。大多数基于潜在因素模型的上下文感知方法都假定上下文与用户和项目共享相同的潜在空间。然而，这样的策略并不总是有意义的，例如，上下文信息的影响可能被高估了。在本文中，我们提出了一个通用框架来学习上下文感知的潜在表示，以进行上下文感知的协同过滤，而无需将上下文强加到用户和项目的潜在空间中。上下文内容通过函数进行组合以生成上下文影响因素，然后将其与每个潜在因素进行组合以得出潜在表示。我们使用偏见矩阵分解对评分预测任务进行实例化，对贝叶斯个性化排名（BPR）进行项目推荐任务来实例化通用框架。通过共同学习每个上下文的权重以及用户和项目的潜在因素，开发了基于随机梯度下降（SGD）的优化程序以适合两个上下文感知模型。在三个真实世界的数据集上进行的实验表明，我们的上下文感知CF模型不仅明显优于基本模型，而且还具有代表性。通过共同学习每个上下文的权重以及用户和项目的潜在因素，开发了基于随机梯度下降（SGD）的优化程序以适合两个上下文感知模型。在三个真实世界的数据集上进行的实验表明，我们的上下文感知CF模型不仅明显优于基本模型，而且还具有代表性。通过共同学习每个上下文的权重以及用户和项目的潜在因素，开发了基于随机梯度下降（SGD）的优化程序以适合两个上下文感知模型。在三个真实世界的数据集上进行的实验表明，我们的上下文感知CF模型不仅明显优于基本模型，而且还具有代表性。