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Recommending cold items remains a significant challenge in billion-scale online recommendation systems.
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How to learn item representation for cold-start multimedia recommendation?. In International Conference on Multimedia (MM)
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Recommendation for new users and new items via randomized training and mixture-of-experts transformation. In Conference on Information Retrieval (SIGIR)
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How Can Recommender Systems Benefit from Large Language Models: A Survey
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Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation. In Conference on Information Retrieval (SIGIR)
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Large language models are competitive near cold-start recommenders for language-and item-based preferences. In Conference on Recommender Systems (RecSys)
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Contrastive learning for cold-start recommendation. In International Conference on Multimedia (MM)
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Generative adversarial framework for cold-start item recommendation. In Conference on Information Retrieval (SIGIR)
Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, and Zhoujun Li. 2022 · 2022
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Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework. In International World Wide Web Conference (WWW)
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Gorec: a generative cold-start recommendation framework. In Proceedings of the 31st ACM international conference on multimedia . 1004–1012
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User cold-start recommendation via inductive heterogeneous graph neural network
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Aligning Distillation For Cold-Start Item Recommendation. In Conference on Information Retrieval (SIGIR)
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Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination. In The Thirty-eighth Annual Conference on Neural Information Processing Systems
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A survey on large language models for recommendation
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Graph Neural Patching for Cold-Start Recommendations. In Australasian Database Conference . Springer, 334–346
Hao Chen, Yu Yang, Yuanchen Bei, Zefan Wang, Yue Xu, and Feiran Huang. 2025 · 2025
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