Abstract
Both recommender systems and knowledge graphs can provide overall and detailed views on datasets, and each of them has been a hot research domain by itself. However, recommending items with a pre-constructed knowledge graph or without one often limits the recommendation performance. Similarly, constructing and completing a knowledge graph without a target is insufficient for applications, such as recommendation. In this paper, we address the problems of recommendation together with knowledge graph completion by a novel model named RecKGC that generates a completed knowledge graph and recommends items for users simultaneously. Comprehensive representations of users, items and interactions/relations are learned in each respective domain, such as our attentive embeddings that integrate tuples in a knowledge graph for recommendation and our high-level interaction representations of entities and relations for knowledge graph completion. We join the tasks of recommendation and knowledge graph completion by sharing the comprehensive representations. As a result, the performance of recommendation and knowledge graph completion are mutually enhanced, which means that the recommendation is getting more effective while the knowledge graph is getting more informative. Experiments validate the effectiveness of the proposed model on both tasks.
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Ma, J., Zhong, M., Wen, J., Chen, W., Zhou, X., Li, X. (2019). RecKGC: Integrating Recommendation with Knowledge Graph Completion. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_18
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