Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a recommendation strategy that more closely mimics the objectives of human curators. We consider multiple data sources to enhance the recommendation process, as well as the quality and diversity of the provided suggestions. Further, we pair each suggestion with an explanation that showcases why a book was recommended with the aim of easing the decision making process for the user. Empirical studies using Social Book Search data demonstrate the effectiveness of the proposed methodology.
Copyright © 2018 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
Dragovic, Nevena; Madrazo Azpiazu, Ion; and Pera, Maria Soledad. (2018). "From Recommendation to Curation: When the System Becomes Your Personal Docent". IntRS 2018: 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, 2225, 37-44.