Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities

• We identify shortcomings of current recommender systems.
• We present an interactive recommender framework to tackle the shortcomings.
• We analyze existing interactive recommenders along the dimensions of our framework.
• Based on the analysis, we identify future research challenges and opportunities.

Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.