Review-Enhanced Hierarchical Contrastive Learning for Recommendation
Authors: Ke Wang, Yanmin Zhu, Tianzi Zang, Chunyang Wang, Mengyuan Jing
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the superiority of Re HCL compared with state-of-the-arts. Extensive experiments are conducted on three datasets to verify the superiority of Re HCL over strong baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China 2Hangzhou Innovation Institute, Beihang University, Hangzhou, China 3Nanjing University of Aeronautics and Astronautics, Nanjing, China |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of its source code. |
| Open Datasets | Yes | We evaluate our model on Amazon dataset (Mc Auley and Leskovec 2013)1, which contains ratings and user-generated reviews. 1http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | Following previous studies (Chen et al. 2018; Shuai et al. 2022), we randomly split the user item pairs of each dataset into 80% training set, 10% validation set, and 10% testing set. |
| Hardware Specification | No | The paper states 'Re HCL is implemented with Tensorflow' but does not specify any particular hardware (e.g., GPU model, CPU, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions 'Re HCL is implemented with Tensorflow' and 'Adam optimizer' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | Re HCL is implemented with Tensorflow. We adopt Adam optimizer with an initial learning rate of 10 3. The layer number is 3 and the embedding size is 64. We used the L2 regularization and its weight β3 is set to 10 4. |