CLLMRec: Contrastive Learning with LLMs-based View Augmentation for Sequential Recommendation
Authors: Fan Lu, Xiaolong Xu, Haolong Xiang, Lianyong Qi, Xiaokang Zhou, Fei Dai, Wanchun Dou
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three public datasets demonstrate that the proposed method outperforms state-of-the-art baseline models, and significantly enhances recommendation performance. The framework was evaluated on three publicly available datasets, and the results demonstrate that it outperforms state-of-the-art models in all scenarios. Additionally, further ablation experiments validate the effectiveness of the LLMs-based view augmentation method and the contrastive learning module. |
| Researcher Affiliation | Academia | 1 Nanjing University of Information Science and Technology 2 China University of Petroleum (East China) 3 Kansai University 4 Southwest Forestry University 5 Nanjing University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The pseudocode for the overall algorithm is presented in Algorithm 1. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | Datasets. The experiments were conducted using three publicly available recommendation system datasets: ML-1M, Beauty, and Steam, which cover multiple domains including movies, cosmetics, and games, making them highly representative. |
| Dataset Splits | No | The paper mentions evaluating NDCG@K (full corpus) and discusses a view augmentation rate of 0.75, but it does not specify explicit training/test/validation dataset splits (e.g., percentages or counts) for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer and cross-entropy loss function, but it does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Models are trained using the cross-entropy loss function and the Adam W optimizer, with a batch size set at 128, a learning rate of 1e-3, and a maximum of 2000 training epochs. Validation occurs every 10 epochs during training. The view augmentation rate α is set at 0.75, and the contrastive learning weight wi is set at 0.5. Early stopping is employed when Recall@10 shows no improvement for 20 consecutive validation rounds. |