SemStim: Exploiting knowledge graphs for cross-domain recommendation

In this paper we introduce SemStim, an unsupervised graph-based algorithm that addresses the cross-domain recommendation task. In this task, preferences from one conceptual domain (e.g. movies) are used to recommend items belonging to another domain (e.g. music). SemStim exploits the semantic links found in a knowledge graph (e.g. DBpedia), to connect domains and thus generate recommendations. As a key benefit, our algorithm does not require (1) ratings in the target domain, thus mitigating the cold-start problem and (2) overlap between users or items from the source and target domains. In contrast, current state-of-the-art personalisation approaches either have an inherent limitation to one domain or require rating data in the source and target domains. We evaluate SemStim by comparing its accuracy to state-of-the-art algorithms for the top-k recommendation task, for both single-domain and cross-domain recommendations. We show that SemStim enables cross-domain recommendation, and that in addition, it has a significantly better accuracy than the baseline algorithms.