TagRec

Abstract

In this paper, we introduce TagRec, a standardized tag recommender benchmarking framework implemented in Java. The purpose of TagRec is to provide researchers with a framework that supports all steps of the development process of a new tag recommendation algorithm in a reproducible way, including methods for data pre-processing, data modeling, data analysis and recommender evaluation against state-of-the-art baseline approaches. We show the performance of the algorithms implemented in TagRec in terms of prediction quality and runtime using an evaluation of a real-world folksonomy dataset. Furthermore, TagRec contains two novel tag recommendation approaches based on models derived from human cognition and human memory theories.