1 |
HE R, McAULEY J. VBPR: visual Bayesian personalized ranking from implicit feedback[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 144-150.
|
2 |
CHEN J, ZHANG H, HE X, et al. Attentive collaborative filtering: multimedia recommendation with item-and component-level attention[C]// Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. New York: ACM, 2017: 335-344.
|
3 |
WANG X, HE X, WANG M, et al. Neural graph collaborative filtering[C]// Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2019: 165-174.
|
4 |
HE X, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[C]// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020: 639-648.
|
5 |
FAN W, MA Y, LI Q, et al. Graph neural networks for social recommendation[C]// Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 417-426.
|
6 |
WANG X, HE X, CAO Y, et al. KGAT: knowledge graph attention network for recommendation[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 950-958.
|
7 |
DELDJOO Y, SCHEDL M, CREMONESI P, et al. Recommender systems leveraging multimedia content[J]. ACM Computing Surveys, 2021, 53(5): No.106.
|
8 |
WEI Y, WANG X, NIE L, et al. MMGCN: multi-modal graph convolution network for personalized recommendation of micro-video[C]// Proceedings of the 27th ACM International Conference on Multimedia. New York: ACM, 2019: 1437-1445.
|
9 |
TAO Z, WEI Y, WANG X, et al. MGAT: multimodal graph attention network for recommendation[J]. Information Processing and Management, 2020, 57(5): No.102277.
|
10 |
WANG Q, WEI Y, YIN J, et al. DualGNN: dual graph neural network for multimedia recommendation[J]. IEEE Transactions on Multimedia, 2023, 25: 1074-1084.
|
11 |
ZHANG J, ZHU Y, LIU Q, et al. Mining latent structures for multimedia recommendation[C]// Proceedings of the 29th ACM International Conference on Multimedia. New York: ACM, 2021: 3872-3880.
|
12 |
ZHOU X, SHEN Z. A tale of two graphs: freezing and denoising graph structures for multimodal recommendation[C]// Proceedings of the 31st ACM International Conference on Multimedia. New York: ACM, 2023: 935-943.
|
13 |
ZHOU H, ZHOU X, ZENG Z, et al. A comprehensive survey on multimodal recommender systems: taxonomy, evaluation, and future directions[EB/OL]. [2024-02-09]..
|
14 |
LIU F, CHENG Z, SUN C, et al. User diverse preference modeling by multimodal attentive metric learning[C]// Proceedings of the 27th ACM International Conference on Multimedia. New York: ACM, 2019: 1526-1534.
|
15 |
LIU S, CHEN Z, LIU H, et al. User-video co-attention network for personalized micro-video recommendation[C]// Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 3020-3026.
|
16 |
WEI Y, WANG X, NIE L, et al. Graph-refined convolutional network for multimedia recommendation with implicit feedback[C]// Proceedings of the 28th ACM International Conference on Multimedia. New York: ACM, 2020: 3541-3549.
|
17 |
MU Z, ZHUANG Y, TAN J, et al. Learning hybrid behavior patterns for multimedia recommendation[C]// Proceedings of the 30th ACM International Conference on Multimedia. New York: ACM, 2022: 376-384.
|
18 |
ZHU Y, XU W, ZHANG J, et al. A survey on graph structure learning: progress and opportunities[EB/OL]. [2024-03-04]..
|
19 |
CHEN Y, WU L, ZAKI M J. Iterative deep graph learning for graph neural networks: better and robust node embeddings[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2020: 19314-19326.
|
20 |
ZHAO J, WANG X, SHI C, et al. Heterogeneous graph structure learning for graph neural networks[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2021: 4697-4705.
|
21 |
SAHA A, MENDEZ O, RUSSELL C, et al. Learning adaptive neighborhoods for graph neural networks[C]// Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 22484-22493.
|
22 |
LUO D, CHENG W, YU W, et al. Learning to drop: robust graph neural network via topological denoising[C]// Proceedings of the 14th ACM International Conference on Web Search and Data Mining. New York: ACM, 2021: 779-787.
|
23 |
KREUZER D, BEAINI D, HAMILTON W L, et al. Rethinking graph transformers with spectral attention[C]// Proceedings of the 35th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2021: 21618-21629.
|
24 |
ZHOU H, ZHOU X, ZHANG L, et al. Enhancing dyadic relations with homogeneous graphs for multimodal recommendation[C]// Proceedings of the 26th European Conference on Artificial Intelligence. Amsterdam: IOS Press, 2023: 3123-3130.
|
25 |
WANG X, JI H, SHI C, et al. Heterogeneous graph attention network[C]// Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 2022-2032.
|