SemStim at the Linked Open Data-enabled Recommender Systems 2014 challenge

Abstract

SemStim is a graph-based recommendation algorithm which is based on Spreading Activation and adds targeted activation and duration constraints. SemStim is not affected by data sparsity, the cold-start problem or data quality issues beyond the linking of items to DBpedia. The overall results show that the performance of SemStim for the diversity task of the challenge is comparable to the other participants, as it took 3rd place out of 12 participants with 0.0413 F1@20 and 0.476 ILD@20. In addition, as SemStim has been designed for the requirements of cross-domain rec- ommendations with different target and source domains, this shows that SemStim can also provide competitive single-domain recommendations.