# The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

## TL;DR

The paper introduces TagRec, an open-source toolkit designed to facilitate the development and evaluation of tag-based recommender systems, demonstrated through four practical use cases and supporting large-scale research projects.

## Contribution

It presents the current state of TagRec, highlighting its versatility and application in various tag-based recommendation tasks and evaluations.

## Key findings

- Supported development in two large-scale European projects
- Implemented four diverse use cases for tag recommendations
- Contributed to 17 research papers on tag-based systems

## Abstract

Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems, a number of recommender frameworks have been proposed that serve a wide range of application domains. Our TagRec framework is one of the few examples of an open-source framework tailored towards developing and evaluating tag-based recommender systems. In this paper, we present the current, updated state of TagRec, and we summarize and reflect on four use cases that have been implemented with TagRec: (i) tag recommendations, (ii) resource recommendations, (iii) recommendation evaluation, and (iv) hashtag recommendations. To date, TagRec served the development and/or evaluation process of tag-based recommender systems in two large scale European research projects, which have been described in 17 research papers. Thus, we believe that this work is of interest for both researchers and practitioners of tag-based recommender systems.