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The development of large vision-language models (LVLMs) offers the potential to address challenges faced by traditional multimodal recommendations thanks to their proficient understanding of static images and textual dynamics.
VBPR: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI conference on artificial intelligence , Vol. 30
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 765–774
Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 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.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
Earlier work this paper cites.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
Earlier work this paper cites.
Disentangled multimodal representation learning for recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng, Anan Liu, Liqiang Nie, and Mohan Kankanhalli. 2022a · 2022
Earlier work this paper cites.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Earlier work this paper cites.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.
Mm-rec: Visiolinguistic model empowered multimodal news recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2560–2564
Chuhan Wu, Fangzhao Wu, Tao Qi, Chao Zhang, Yongfeng Huang, and Tong Xu. 2022 · 2022
Earlier work this paper cites.
Latent structure mining with contrastive modality fusion for multimedia recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, and Liang Wang. 2022b · 2022
Earlier work this paper cites.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022a · 2022
Cited alongside, same era.
Yunkang Cao, Xiaohao Xu, Chen Sun, Xiaonan Huang, and Weiming Shen. 2023 · 2023
Cited alongside, same era.
VIP5: Towards Multimodal Foundation Models for Recommendation
Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, and Yongfeng Zhang. 2023 · 2023
Cited alongside, same era.
Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023 · 2023
Cited alongside, same era.
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
Yu Wang, Zhiwei Liu, Jianguo Zhang, Weiran Yao, Shelby Heinecke, and Philip S Yu. 2023a · 2023
Later among the works it cites.
Zhanyu Wang, Longyue Wang, Zhen Zhao, Minghao Wu, Chenyang Lyu, Huayang Li, Deng Cai, Luping Zhou, Shuming Shi, and Zhaopeng Tu. 2023b · 2023
Later among the works it cites.
On the road with GPT-4V (ision): Early explorations of visual-language model on autonomous driving
Licheng Wen, Xuemeng Yang, Daocheng Fu, Xiaofeng Wang, Pinlong Cai, Xin Li, Tao Ma, Yingxuan Li, Linran Xu, Dengke Shang, et al · 2023
Later among the works it cites.
Multimodal chatgpt for medical applications: an experimental study of gpt-4v
Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, and Lichao Sun. 2023 · 2023
Later among the works it cites.
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023b · 2023
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Finding supporting examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
Cited alongside, same era.
Deductive Verification of Chain-of-Thought Reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. 2023 · 2023
Cited alongside, same era.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023a · 2023
Cited alongside, same era.
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation
Han Liu, Yinwei Wei, Fan Liu, Wenjie Wang, Liqiang Nie, and Tat-Seng Chua. 2023b · 2023
Cited alongside, same era.
Megcf: Multimodal entity graph collaborative filtering for personalized recommendation
Kang Liu, Feng Xue, Dan Guo, Le Wu, Shujie Li, and Richang Hong. 2023c · 2023
Cited alongside, same era.
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Kenji Ikamura, Georg Gerber, Ivy Liang, Long Phi Le, Tong Ding, Anil V Parwani, et al · 2023
Cited alongside, same era.
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. 2023 · 2023
Cited alongside, same era.
The dawn of lmms: Preliminary explorations with gpt-4v (ision)
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. 2023b · 2023
Later among the works it cites.
Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing. 2023a · 2023
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Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
Lingzi Zhang, Xin Zhou, and Zhiqi Shen. 2023b · 2023
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Hongyu Zhou, Xin Zhou, Zhiwei Zeng, Lingzi Zhang, and Zhiqi Shen. 2023c · 2023
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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. 2023b · 2023
Later among the works it cites.
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation
Yan Zhou, Jie Guo, Hao Sun, Bin Song, and Fei Richard Yu. 2023a · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models
Rohan Wadhawan, Hritik Bansal, Kai-Wei Chang, and Nanyun Peng. 2024 · 2024
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