Topics include, but are not limited to, the following: Human Contributions beyond the User-Item Matrix · Applications and interfaces for collecting annotations, · Games With A Purpose (GWAP) or other annotation-as-by-product designs, · Effective Learning from crowd-annotated or crowd-augmented datasets, · Mining social media to support recommendation, · Conversational recommender systems, · Wisdom of the Crowd for decisions support. Designing and Evaluating Recommenders using Crowd Techniques · Recommender evaluation metrics and studies, · Crowd-based user studies, · Human intelligence for personalization support, · User modeling and profiling. Methodologies for Human Intelligence in Recommender Systems · Identifying expertise and managing reputation, · Engaging crowdmembers and ensuring quality, · Tools and platforms to support crowd-enhanced Recommender Systems, · Inherent biases, limitations and trade-offs of crowd-powered approaches, · Empirical and case studies of crowd-enhanced recommendation, · Ethical, cultural and policy issues related to crowd recommendation.