지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.18 No.3
- 발행연도
- 2024.9
- 수록면
- 169 - 180 (12page)
- DOI
- 10.5626/JCSE.2024.18.3.169
이용수
초록· 키워드
오류제보하기
Information overload and complex user interactions make it difficult to retrieve valuable data. Recommendation systems have become crucial in addressing this challenge by providing users with relevant information and items. Collaborative filtering-based recommendation methods, which are commonly used in this context, often suffer from data scarcity, thus limiting their effectiveness for users with insufficient interaction data. To overcome this problem, knowledge graphs have been integrated into recommendation systems to enhance user and item representation through the implementation of additional semantic relatedness. Despite their potential utility, most recommendation models assume binary relations within knowledge graphs, thereby overlooking the high-order relationships that are prevalent in knowledge graphs. Knowledge hypergraphs, which can capture complex and multi-dimensional relationships, offer a solution to this limitation. This paper proposes KHG-Aclair, a novel recommendation system that leverages hypergraphs to uncover hidden features within knowledge graphs, thus enhancing recommendation accuracy and insight. We have transformed the Freebase knowledge graph into a knowledge hypergraph and made this dataset publicly available. KHG-Aclair also incorporates contrastive learning to refine the knowledge hypergraph, thus reducing noise and improving representation for less popular items. Altogether, our model demonstrates strong generalizability, as it achieves high performance across multiple datasets, thus indicating that it can serve as a versatile solution for various recommendation systems. Our implementation codes are available at https://github.com/HBD-NGC1316/KHG-Aclair.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORK
Ⅲ. PRELIMINARIES
Ⅳ. METHODOLOGY
Ⅴ. EXPERIMENTS
Ⅵ. RESULTS AND ANALYSIS
Ⅶ. FUTURE DIRECTIONS
Ⅷ. CONCLUSION
REFERENCES
참고문헌 (23)
참고문헌 신청
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in Proceedings of the World Wide Web Conference, San Francisco, CA, USA, 2019, pp. 417-426. https://doi.org/10.1145/3308558.3313488
C. Huang, “Recent advances in heterogeneous relation learning for recommendation,” 2021 [Online]. Available: https://arxiv.org/abs/2110.03455.
R. Togashi, M. Otani, and S. I. Satoh, “Alleviating cold-start problems in recommendation through pseudo-labelling over knowledge graph,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, 2021, pp. 931-939. https://doi.org/10.1145/3437963.3441773
C. Huang, H. Xu, Y. Xu, P. Dai, L. Xia, M. Lu, et al., “Knowledge-aware coupled graph neural network for social recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4115-4122, 2021. https://doi.org/10.1609/aaai.v35i5.16533
X. Wang, X. He, Y. Cao, M. Liu, and T. S. Chua, “KGAT: knowledge graph attention network for recommendation,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 950-958. https://doi.org/10.1145/3292500.3330989
C. Huang, “Recent advances in heterogeneous relation learning for recommendation,” 2021 [Online]. Available: https://arxiv.org/abs/2110.03455.
R. Togashi, M. Otani, and S. I. Satoh, “Alleviating cold-start problems in recommendation through pseudo-labelling over knowledge graph,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, 2021, pp. 931-939. https://doi.org/10.1145/3437963.3441773
C. Huang, H. Xu, Y. Xu, P. Dai, L. Xia, M. Lu, et al., “Knowledge-aware coupled graph neural network for social recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4115-4122, 2021. https://doi.org/10.1609/aaai.v35i5.16533
X. Wang, X. He, Y. Cao, M. Liu, and T. S. Chua, “KGAT: knowledge graph attention network for recommendation,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 950-958. https://doi.org/10.1145/3292500.3330989
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