Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures

In this paper we investigate the incorporation of graph-based features into LOD path-based recommender systems, an approach that so far has received little attention. More specifically, we propose two normalisation procedures that adjust user-item path counts by the degree centrality of the nodes connecting them. Evaluation on the MovieLens 1M dataset shows that the linear normalisation approach yields a significant increase in recommendation accuracy as compared to the default case, especially in settings where the most popular movies are omitted. These results serve as a fruitful base for further incorporation of graph measures into recommender systems, and might help in establishing the recommendation diversity that has recently gained much attention.

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