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Multi-modal recommender systems (MRSs) have achieved notable success in improving personalization by leveraging diverse modalities such as images, text, and audio.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
P Kingma Diederik. 2014 · 2014
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
LRMM: Learning to recommend with missing modalities
Cheng Wang, Mathias Niepert, and Hui Li. 2018 · 2018
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
N Reimers. 2019 · 2019
Earlier work this paper cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of the 27th ACM international conference on multimedia . 1437–1445
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
Club: A contrastive log-ratio upper bound of mutual information. In International conference on machine learning . PMLR, 1779–1788
Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin. 2020 · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
Graph-refined convolutional network for multimedia recommendation with implicit feedback. In Proceedings of the 28th ACM international conference on multimedia . 3541–3549
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2020 · 2020
Earlier work this paper cites.
Joint item recommendation and attribute inference: An adaptive graph convolutional network approach. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 679–688
Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
Dealing with missing modalities in the visual question answer-difference prediction task through knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1592–1601
Jae Won Cho, Dong-Jin Kim, Jinsoo Choi, Yunjae Jung, and In So Kweon. 2021 · 2021
Cited alongside, same era.
Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 726–735
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021 · 2021
Cited alongside, same era.
Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 10790–10797
Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu. 2021 · 2021
Cited alongside, same era.
Mining latent structures for multimedia recommendation. In Proceedings of the 29th ACM international conference on multimedia . 3872–3880
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, and Liang Wang. 2021 · 2021
Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, and Zhiqi Shen. 2023b · 2023
Later among the works it cites.
Mmrec: Simplifying multimodal recommendation. In Proceedings of the 5th ACM International Conference on Multimedia in Asia Workshops . 1–2
Xin Zhou. 2023 · 2023
Later among the works it cites.
A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation. In Proceedings of the 31st ACM International Conference on Multimedia . 935–943
Xin Zhou and Zhiqi Shen. 2023 · 2023
Later among the works it cites.
Bootstrap latent representations for multi-modal recommendation. In Proceedings of the ACM Web Conference 2023 . 845–854
Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, and Feijun Jiang. 2023a · 2023
Later among the works it cites.
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Cited alongside, same era.
Data/model jointly driven high-quality case generation for power system dynamic stability assessment
Lipeng Zhu and David J Hill. 2021 · 2021
Cited alongside, same era.
Self-supervised learning for multimedia recommendation
Zhulin Tao, Xiaohao Liu, Yewei Xia, Xiang Wang, Lifang Yang, Xianglin Huang, and Tat-Seng Chua. 2022 · 2022
Cited alongside, same era.
Are graph augmentations necessary? simple graph contrastive learning for recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval . 1294–1303
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. 2022 · 2022
Cited alongside, same era.
Latent structure mining with contrastive modality fusion for multimedia recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, and Liang Wang. 2022 · 2022
Cited alongside, same era.
Contrastive intra-and inter-modality generation for enhancing incomplete multimedia recommendation. In Proceedings of the 31st ACM International Conference on Multimedia . 6234–6242
Zhenghong Lin, Yanchao Tan, Yunfei Zhan, Weiming Liu, Fan Wang, Chaochao Chen, Shiping Wang, and Carl Yang. 2023 · 2023
Cited alongside, same era.
Multi-modal self-supervised learning for recommendation. In Proceedings of the ACM Web Conference 2023 . 790–800
Wei Wei, Chao Huang, Lianghao Xia, and Chuxu Zhang. 2023 · 2023
Cited alongside, same era.
Multi-view graph convolutional network for multimedia recommendation. In Proceedings of the 31st ACM International Conference on Multimedia . 6576–6585
Penghang Yu, Zhiyi Tan, Guanming Lu, and Bing-Kun Bao. 2023 · 2023
Cited alongside, same era.
Where to go next for recommender systems? id-vs. modality-based recommender models revisited. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2639–2649
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
Cited alongside, same era.
Multimodality invariant learning for multimedia-based new item recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 677–686
Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, and Meng Wang. 2024 · 2024
Later among the works it cites.
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios. In Proceedings of the 18th ACM Conference on Recommender Systems . 380–390
Christian Ganhör, Marta Moscati, Anna Hausberger, Shah Nawaz, and Markus Schedl. 2024 · 2024
Later among the works it cites.
LGMRec: Local and Global Graph Learning for Multimodal Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 8454–8462
Zhiqiang Guo, Jianjun Li, Guohui Li, Chaoyang Wang, Si Shi, and Bin Ruan. 2024 · 2024
Later among the works it cites.
Large language models meet collaborative filtering: An efficient all-round llm-based recommender system. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1395–1406
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, and Chanyoung Park. 2024 · 2024
Later among the works it cites.
GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 1400–1409
Guojiao Lin, Meng Zhen, Dongjie Wang, Qingqing Long, Yuanchun Zhou, and Meng Xiao. 2024 · 2024
Later among the works it cites.
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D Malliaros, and Tommaso Di Noia. 2024b · 2024
Later among the works it cites.
Deep multimodal learning with missing modality: A survey
Renjie Wu, Hu Wang, Hsiang-Ting Chen, and Gustavo Carneiro. 2024 · 2024
Later among the works it cites.
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 3645–3656
Guipeng Xv, Xinyu Li, Ruobing Xie, Chen Lin, Chong Liu, Feng Xia, Zhanhui Kang, and Leyu Lin. 2024 · 2024
Later among the works it cites.