Context-Aware Recommender Systems (CARS) improve traditional Recommender Systems (RS) in a wide array of domains and applications. However, CARS suffer from several inherent issues such as data sparsity and cold start. Incorporating trust into recommender systems can handle these issues. Trust-aware recommender systems use information from social networks such as trust statements, which prove another valuable information source. This paper exploits the advantages of these two systems by incorporating both trust and context information. We propose a hybrid approach: Trust based Context aware Post Filtering Approach that uses trust statements as a rich information with context compensation method of contextual post-filtering approach. Our approach utilizes the relative average difference among the context on output of trust aware collaborative filtering by incorporating explicit and implicit trust information. We also use a confidence concept to remove non-confident users from the trust network before generating prediction. The performed experiments show that the proposed approach improves upon the standard RS and outperforms recommendation approaches on real world dataset.

上下文感知推荐系统（CARS）在众多领域和应用程序中改进了传统推荐系统（RS）。但是，CARS遭受一些固有的问题，例如数据稀疏和冷启动。将信任合并到推荐系统中可以解决这些问题。信任感知的推荐系统使用来自社交网络的信息，例如信任声明，这些信息证明了另一个有价值的信息源。本文通过结合信任和上下文信息来利用这两个系统的优势。我们提出了一种混合方法：基于信任的上下文感知后过滤方法，该方法使用信任声明作为丰富的信息以及上下文后过滤方法的上下文补偿方法。我们的方法通过结合显式和隐式信任信息，利用信任感知协作过滤输出中上下文之间的相对平均差异。我们还使用置信度概念在生成预测之前从信任网络中删除不信任的用户。所进行的实验表明，所提出的方法对标准RS进行了改进，并且优于真实世界数据集上的推荐方法。