Towards Explainable Conversational Recommendation
Authors: Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment 4.1 Experimental Settings Dataset. As shown in Table 1, we use three publicly available datasets. Electronics and Movie&TV are two categories of the Amazon dataset2, and Yelp contains restaurant reviews from Yelp Challenge 20163. Each dataset is split into a training set (80%), a validation set (10%) and a test set (10%). |
| Researcher Affiliation | Collaboration | Zhongxia Chen1,2 , Xiting Wang2 , Xing Xie2 , Mehul Parsana3 , Akshay Soni3 , Xiang Ao4 and Enhong Chen1 1School of Computer Science and Technology, University of Science and Technology of China 2Microsoft Research Asia 3Microsoft Bing Ads 4Institute of Computing Technology, Chinese Academy of Sciences |
| Pseudocode | No | The paper describes the model in detail with text and mathematical equations, but it does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | Yes | Dataset. As shown in Table 1, we use three publicly available datasets. Electronics and Movie&TV are two categories of the Amazon dataset2, and Yelp contains restaurant reviews from Yelp Challenge 20163. 2http://jmcauley.ucsd.edu/data/amazon/ 3https://www.yelp.com/dataset/challenge |
| Dataset Splits | Yes | Each dataset is split into a training set (80%), a validation set (10%) and a test set (10%). The training set is used to derive Ωu, which consists of items that u provided reviews for. Following [Li et al., 2017; Chen et al., 2019], we consider the ground-truth explanations as the first sentence in the reviews. The validation set is used for model hyperparameter tuning and the test set is leveraged for simulating conversations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory specifications) used to run the experiments. |
| Software Dependencies | No | The paper mentions using Adam as an optimizer and BERT embeddings, but it does not specify version numbers for any software, libraries, or frameworks used. |
| Experiment Setup | Yes | Most hyperparameters are set and tuned by following the papers of the baselines [Li et al., 2017; Chen et al., 2019]. The λs for balancing different losses are set to 1 except for λc=0.05. We tune np by performing grid search over {1, 2, .., 5}. The learning rates of LΩand LF are set to 10 3 and 10 2, respectively. αt is set to 0.8, and αc, αr are initialized by using 0.9 and automatically tuned during the learning process. |