Automated recommendations have become a pervasive feature of our online user experience. Historically, the two main approaches of building recommender systems are collaborative filtering (CF) and content-based filtering (CB). In more recent years, this dichotomy has become more and more blurred, and we observe various attempts to incorporate additional side information and external knowledge sources into the recommendation process, regardless of the adopted recommendation approach. This side information predominantly contains additional knowledge about the recommendable items, e.g., in terms of their features, metadata, category assignments, relations to other items, user-provided tags and comments, or related textual or multimedia content.

The goal of the special issue is to highlight recent progress in the area of recommender systems that propose novel approaches to identify, extract, process, and leverage information about the items in the recommendation process.