Abstract

Information Retrieval and Recommender Systems have been frequently evaluated using indexes based on variants and extensions of precision-like measures. Likewise, approaches for diversity evaluation have been proposed. However, these measures are usually defined in terms of a set of high level content descriptors known as information nuggets that are hard to obtain. We propose a method to create these nuggets using social tags, providing datasets with annotations to evaluate content diversity in recommender systems. Since recommending items to a target user is analogous to searching documents from a query, this method might be extended to Information Retrieval