Semantic Movie Recommendations
SMART INFORMATION SYSTEMS: COMPUTATIONAL INTELLIGENCE FOR REAL-LIFE APPLICATIONS(2015)
摘要
The overwhelming amount of video and audio content makes it difficult for users to find new high-quality content matching the individual preferences. Recommender systems are built to suggest potentially interesting items by computing the similarity between users and items. The big challenges while creating recommender systems are the sparsity of data (the knowledge about users and items is often limited) and the popularity bias (most recommender algorithms tend to recommend popular items already known to the user). Semantic techniques supporting the graph-based representation of knowledge and the integration of heterogeneous datasets allow us to overcome these problems. The aggregation of knowledge from several different sources enables us to take into account many different aspects while computing recommendations. In addition, semantic recommender systems can provide explanations for suggested items helping the user to understand why an unknown item matches the individual user preferences. In this chapter we discuss the challenges in creating recommender systems and explain semantic approaches for the recommendation domain. We discuss the steps for building a semantic recommender system and present a semantic movie recommender system in detail. The advantages of semantic recommender systems compared to traditional recommender approaches are analyzed.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络