In this paper, TagRec, a standardized tag recommender benchmarking framework implemented in Java, is introduced. 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. This paper demonstrates the performance of the algorithms implemented in TagRec in terms of prediction quality and runtime using an extensive 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.
The framework is freely available at Github and has been developed in the frame of the EU project Learning Layers.
Find out more about “TagRec: Towards A Standardized Tag Recommender Benchmarking Framework” here.
Link to the framework on Github: https://github.com/learning-layers/TagRec