1 |
KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems. Computer, 2009, 42(8): 30- 37.
|
2 |
KABBUR S, NING X, KARYPIS G. FISM: factored item similarity models for top-N recommender systems[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2013: 659-667.
|
3 |
HE X N, HE Z K, SONG J K, et al. NAIS: neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2354- 2366.
|
4 |
WANG X, HE X N, 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, USA: ACM Press, 2019: 165-174.
|
5 |
HE X N, 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, USA: ACM Press, 2020: 639-648.
|
6 |
LIU M, NIE L Q, WANG X A, et al. Online data organizer: micro-video categorization by structure-guided multimodal dictionary learning. IEEE Transactions on Image Processing, 2019, 28(3): 1235- 1247.
|
7 |
|
8 |
HE R N, MCAULEY J. VBPR: visual Bayesian personalized ranking from implicit feedback. Artificial Intelligence, 2016, 30(1): 1- 12.
|
9 |
CHEN J Y, ZHANG H W, HE X N, 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, USA: ACM Press, 2017: 335-344.
|
10 |
SUN R, CAO X Z, ZHAO Y, et al. Multi-modal knowledge graphs for recommender systems[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York, USA: ACM Press, 2020: 1405-1414.
|
11 |
WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2019: 950-958.
|
12 |
WU S W, SUN F, ZHANG W T, et al. Graph neural networks in recommender systems: a survey. ACM Computing Surveys, 2022, 55(5): 1- 37.
|
13 |
TAO Z L, WEI Y W, WANG X, et al. MGAT: multimodal graph attention network for recommendation. Information Processing & Management, 2020, 57(5): 102277.
|
14 |
DONG J F, LI X R, SNOEK C G M. Predicting visual features from text for image and video caption retrieval. IEEE Transactions on Multimedia, 2018, 20(12): 3377- 3388.
|
15 |
|
16 |
GABEUR V, SUN C, ALAHARI K, et al. Multi-modal transformer for video retrieval. Berlin, Germany: Springer, 2020: 214- 229.
|
17 |
LI X R, XU C X, YANG G, et al. W2VV++: fully deep learning for ad-hoc video search[C]//Proceedings of the 27th ACM International Conference on Multimedia. New York, USA: ACM Press, 2019: 1786-1794.
|
18 |
LI X R, ZHOU F M, XU C X, et al. SEA: sentence encoder assembly for video retrieval by textual queries. IEEE Transactions on Multimedia, 2021, 23, 4351- 4362.
|
19 |
|
20 |
CAO Y X, WANG X, HE X N, et al. Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences[C]//Proceedings of World Wide Web Conference. New York, USA: ACM Press, 2019: 151-161.
|
21 |
CHEN T, HE X N, KAN M Y. Context-aware image tweet modelling and recommendation[C]//Proceedings of the 24th ACM International Conference on Multimedia. New York, USA: ACM Press, 2016: 1018-1027.
|
22 |
GAO J Y, ZHANG T Z, XU C S. A unified personalized video recommendation via dynamic recurrent neural networks[C]//Proceedings of the 25th ACM International Conference on Multimedia. New York, USA: ACM Press, 2017: 127-135.
|
23 |
潘华莉, 谢珺, 高婧, 等. 融合多模态特征的深度强化学习推荐模型. 数据分析与知识发现, 2023, 7(4): 114- 128.
|
|
PAN H L, XIE J, GAO J, et al. A deep reinforcement learning recommendation model with multi-modal features. Data Analysis and Knowledge Discovery, 2023, 7(4): 114- 128.
|
24 |
WEI Y W, WANG X, NIE L Q, 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, USA: ACM Press, 2019: 1437-1445.
|
25 |
HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2017: 1025-1035.
|
26 |
胡承佐, 王庆梅, 李迪超, 等. 基于复杂结构信息的图神经网络序列推荐算法. 计算机工程, 2022, 48(5): 82-90, 97.
URL
|
|
HU C Z, WANG Q M, LI D C, et al. Sequence recommendation algorithm of graph neural networks based on complex structure information. Computer Engineering, 2022, 48(5): 82-90, 97.
URL
|
27 |
|
28 |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2016: 770-778.
|