과제정보
이 논문은 2024 년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원과 한국연구재단의 지원을 받아 수행된 연구임(No.2022-0-00352, No.RS-2022-00155586, No.2018R1A5A7059549)
참고문헌
- Chae, Dong-Kyu, et al. "Rating augmentation with generative adversarial networks towards accurate collaborative filtering." The World Wide Web Conference. 2019.
- Chae, Dong-Kyu, et al. "AR-CF: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems." Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020.
- Kong, Taeyong, et al. "Linear, or non-linear, that is the question!." Proceedings of the fifteenth ACM international conference on web search and data mining. 2022.
- Lim, Hongjun, et al. "AiRS: a large-scale recommender system at naver news." 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022.
- Rendle, Steffen, et al. "BPR: Bayesian personalized ranking from implicit feedback." arXiv preprint arXiv:1205.2618 (2012).
- Su, Yixin, et al. "Neural graph matching based collaborative filtering." Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 2021.
- Chen, Lei, et al. "Set2setRank: Collaborative set to set ranking for implicit feedback based recommendation." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021.
- He, Ruining, and Julian McAuley. "VBPR: visual bayesian personalized ranking from implicit feedback." Proceedings of the AAAI conference on artificial intelligence. Vol. 30. No. 1. 2016.
- Wei, Yinwei, et al. "MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video." Proceedings of the 27th ACM international conference on multimedia. 2019.
- Wei, Yinwei, et al. "Graph-refined convolutional network for multimedia recommendation with implicit feedback." Proceedings of the 28th ACM international conference on multimedia. 2020.
- Zhang, Jinghao, et al. "Mining latent structures for multimedia recommendation." Proceedings of the 29th ACM international conference on multimedia. 2021.
- Kim, Taeri, et al. "MARIO: modality-aware attention and modality-preserving decoders for multimedia recommendation." Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022.
- Kim, Yungi, et al. "MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation." Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024.
- Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016).
- Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International conference on machine learning. PMLR, 2020.
- Yu, Penghang, et al. "Multi-view graph convolutional network for multimedia recommendation." Proceedings of the 31st ACM International Conference on Multimedia. 2023.
- Yi, Zixuan, et al. "Multi-modal graph contrastive learning for micro-video recommendation." Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022.
- Zhang, Jinghao, et al. "Latent structure mining with contrastive modality fusion for multimedia recommendation." IEEE Transactions on Knowledge and Data Engineering (2022).
- Tao, Zhulin, et al. "Self-supervised learning for multimedia recommendation." IEEE Transactions on Multimedia (2022).
- Wei, Yinwei, et al. "Contrastive learning for cold-start recommendation." Proceedings of the 29th ACM International Conference on Multimedia. 2021.
- Zhou, Xin, and Zhiqi Shen. "A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation." Proceedings of the 31st ACM International Conference on Multimedia. 2023.
- McAuley, Julian, et al. "Image-based recommendations on styles and substitutes." Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015.