The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.
|Number of pages||4|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 1 Jul 2015|
|Event||23rd Conference on User Modeling, Adaptation, and Personalization, UMAP 2015 - Dublin, Ireland|
Duration: 29 Jun 2015 → 3 Jul 2015