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.
|Keywords||Top-N recommendations, Linked Open Data, Information network schema, Random forest|
|Persistent URL||dx.doi.org/10.1007/978-3-030-19274-7, hdl.handle.net/1765/119673|
van Rossum, B., & Frasincar, F. (2019). Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures. In 19th International Conference on Web Engineering (ICWE 2019) (pp. 19–31). doi:10.1007/978-3-030-19274-7