Graph Embedding Based Recommendation Techniques on the Knowledge Graph

Abstract

This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.

DOI: 10.1145/3099023.3099096

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Cite this paper

@inproceedings{GradGyenge2017GraphEB, title={Graph Embedding Based Recommendation Techniques on the Knowledge Graph}, author={L{\'a}szl{\'o} Grad-Gyenge and Attila Kiss and Peter Filzmoser}, booktitle={UMAP}, year={2017} }