Abstract
Integrating knowledge graphs into recommendation systems is promising as knowledge graphs can be side information to address cold start and data sparsity issues in recommendation systems. However, existing methods largely assume that knowledge graphs are under a closed-world assumption, this may lead to suboptimal performances. Furthermore, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. Therefore, it is crucial to consider the incomplete nature of knowledge graphs as well as to represent hierarchical structure when incorporating it into recommendation system. In this paper, we propose Symbiosis, which is an end-to-end model that utilizes link prediction task in knowledge graphs to assist recommendation task. A general motivation for Symbiosis is that the two tasks automatically share the latent features between items and entities. We also incorporated a hierarchical structure method that maps entities into the polar coordinate system into the Symbiosis. Under this framework, not only users can get better recommendations but also knowledge graphs can be completed as these two tasks have a mutual effect. To evaluate the performance of each component, we conduct extensive experiments with two real-world datasets from different scenarios. The extensive results show that Symbiosis can be trained substantially improving F1-score by 59.7% on movie dataset and MR by 59.3% on music dataset compared to state-of-the-art methods.
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Du, H., Tang, Y. (2021). Symbiosis: A Novel Framework for Integrating Hierarchies from Knowledge Graph into Recommendation System. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_20
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