no code implementations • 9 Feb 2024 • Cong Xu, Zhangchi Zhu, Jun Wang, Jianyong Wang, Wei zhang
Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already.
no code implementations • 13 Jan 2024 • Lu Wang, Chao Du, Pu Zhao, Chuan Luo, Zhangchi Zhu, Bo Qiao, Wei zhang, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
no code implementations • 8 Nov 2023 • Zhangchi Zhu, Wei zhang
In this paper, we decompose recommendation models into three layers, i. e., the input layer, the intermediate layer, and the output layer, and address deficiencies layer by layer.
1 code implementation • 1 Aug 2023 • Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei zhang, Hang Dong, Bo Qiao, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.