Recommending topics for web curation

Abstract

A new generation of curation services provides users with a set of tools to manually curate and manage topical collections of content. However, given curation is ultimately a manual effort, it still requires significant effort on the part of the curator both in terms of collecting and managing content. We are interested in providing additional assistance to users in their curation tasks, in particular when it comes to efficiently adding content to their collection, and examine recommender systems in an effort to automate this task. We examine a number of recommendation strategies using live-user data from the popular Scoop.it curation service. © 2013 Springer-Verlag.