Marcelo Mendoza, Nicolás Torres: “Creating High Level Content Descriptors for Recommender Systems Datasets” (JCC 2018)

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 \textit{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.

Date: November 7th, 2018. 15.00 h.

Venue: Universidad Andrés Bello, Antonio Varas 880, Providencia, Santiago.