  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.
