上下文感知推荐系统:挑战和机遇

该文梳理了社会和科学领域中上下文感知推荐系统的主要概念、技术、挑战和未来趋势；其次，分类介绍了可用于基于上下文的推荐的一系列技术和主要框架。除了经典的基于内容、基于协同过滤和基于矩阵分解的技术之外，调研了最近的研究方向，即基于深度学习和基于模糊逻辑的方法。最后，描述了在推荐过程中利用上下文信息的潜在研究机会。

Context-Aware Recommender Systems: Challenges and Opportunities

In this review, we attempt to highlight major concepts, techniques, challenges and future trends of context-aware recommender systems in social and scientific domains. The primary objective of this paper is to sum up the most recent developments in this rich knowledge area. A set of techniques and major frameworks available for context-based recommender systems are classified and introduced. Along with classical content-based, collaborative filtering and matrix factorization based techniques, we investigate the most recent research areas, i.e., deep learning and fuzzy logic based methodologies. Finally, we close by portraying potential future research opportunities with respect to utilizing context information in recommendation process.