The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
This work provides a toolkit for researchers and practitioners to build tag-based recommender systems, but it is incremental as it updates an existing framework.
The authors present TagRec, an open-source framework for developing and evaluating tag-based recommender systems, which has been used in two large-scale European research projects and contributed to 17 research papers.
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.