CP-Rec: Contextual Prompting for Conversational Recommender Systems
Authors: Keyu Chen, Shiliang Sun
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that it achieves stateof-the-art recommendation accuracy and generates more coherent and informative conversations. Experiments on five datasets demonstrate the superior performance of our method in both recommendation and conversation tasks. |
| Researcher Affiliation | Academia | Keyu Chen, Shiliang Sun* School of Computer Science and Technology, East China Normal University, Shanghai, China 51205901068@stu.ecnu.edu.cn, slsun@cs.ecnu.edu.cn |
| Pseudocode | No | The paper describes the model architecture and components using text and mathematical equations, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that 'The preprocessed datasets and baselines are implemented in CRSLab (Zhou et al. 2021)', but it does not explicitly state that the source code for CP-Rec itself is open-sourced or provide a link to its implementation. |
| Open Datasets | Yes | We use five CRS datasets: (1) Re Dial (Li et al. 2018) (2) Du Rec Dial (Liu et al. 2020) (3) TG-Re Dial (Zhou et al. 2020b) (4) Open Dial KG (Moon et al. 2019) (5) INSPIRED (Hayati et al. 2020) |
| Dataset Splits | No | The paper mentions the datasets used and refers to a 'test set' in the human evaluation section, but it does not explicitly state the training, validation, and test split percentages or sample counts for these datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to conduct the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions using 'pretrained BERT (Devlin et al. 2019)', 'R-GCN (Schlichtkrull et al. 2018)', and 'pretrained GPT2 (Radford et al. 2019)', but it does not provide specific version numbers for these software libraries or models. |
| Experiment Setup | No | The paper describes the model components and objectives, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations necessary for exact reproduction of the experiments. |