TY - GEN
T1 - Knowledge-Aware Hypergraph Neural Network for Recommender Systems
AU - Liu, Binghao
AU - Zhao, Pengpeng
AU - Zhuang, Fuzhen
AU - Xian, Xuefeng
AU - Liu, Yanchi
AU - Sheng, Victor S.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Knowledge graph (KG) has been widely studied and employed as auxiliary information to alleviate the cold start and sparsity problems of collaborative filtering in recommender systems. However, most of the existing KG-based recommendation models suffer from the following drawbacks, i.e., insufficient modeling of high-order correlations among users, items, and entities, and simple aggregation strategies which fail to preserve the relational information in the neighborhood. In this paper, we propose a Knowledge-aware Hypergraph Neural Network (KHNN) framework to tackle the above issues. First, the knowledge-aware hypergraph structure, which is composed of hyperedges, is employed for modeling users, items, and entities in the knowledge graph with explicit hybrid high-order correlations. Second, we propose a novel knowledge-aware hypergraph convolution method to aggregate different knowledge-based neighbors in hyperedge efficiently. Moreover, it can conduct the embedding propagation of high-order correlations explicitly and efficiently in knowledge-aware hypergraph. Finally, we apply the proposed model on three real-world datasets, and the empirical results demonstrate that KHNN can achieve the best improvements against other state-of-the-art methods.
AB - Knowledge graph (KG) has been widely studied and employed as auxiliary information to alleviate the cold start and sparsity problems of collaborative filtering in recommender systems. However, most of the existing KG-based recommendation models suffer from the following drawbacks, i.e., insufficient modeling of high-order correlations among users, items, and entities, and simple aggregation strategies which fail to preserve the relational information in the neighborhood. In this paper, we propose a Knowledge-aware Hypergraph Neural Network (KHNN) framework to tackle the above issues. First, the knowledge-aware hypergraph structure, which is composed of hyperedges, is employed for modeling users, items, and entities in the knowledge graph with explicit hybrid high-order correlations. Second, we propose a novel knowledge-aware hypergraph convolution method to aggregate different knowledge-based neighbors in hyperedge efficiently. Moreover, it can conduct the embedding propagation of high-order correlations explicitly and efficiently in knowledge-aware hypergraph. Finally, we apply the proposed model on three real-world datasets, and the empirical results demonstrate that KHNN can achieve the best improvements against other state-of-the-art methods.
KW - Knowledge graph
KW - Knowledge-aware hypergraph
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85104742225&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73200-4_9
DO - 10.1007/978-3-030-73200-4_9
M3 - Conference contribution
AN - SCOPUS:85104742225
SN - 9783030731991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 132
EP - 147
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Lee, Wang-Chien
A2 - Tseng, Vincent S.
A2 - Kalogeraki, Vana
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Y2 - 11 April 2021 through 14 April 2021
ER -