Disentangled CVAEs with Contrastive Learning for Explainable Recommendation
Authors: Linlin Wang, Zefeng Cai, Gerard de Melo, Zhu Cao, Liang He
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method generates highquality explanations and achieves new state-of-the-art results in diverse domains. |
| Researcher Affiliation | Academia | 1 East China Normal University 2 Hasso Plattner Institute, University of Potsdam 3 East China University of Science and Technology |
| Pseudocode | No | The paper describes the model architecture and training objective using mathematical equations and textual descriptions, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We use three large-scale datasets including Yelp1, Amazon 5-core Movie & TV2 and Trip Advisor3, and follow the common practice (Li, Zhang, and Chen 2020) to extract valid explanations and conduct pre-processing. 1www.yelp.com/dataset 2www.jmcauley.uscd.edu/data/amazon 3www.tripadvisor.com |
| Dataset Splits | No | The paper mentions 'validation loss' indicating the use of a validation set, but it does not specify the explicit percentages or sample counts for the training, validation, and test splits, nor does it provide details on the splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software tools like Sentence-BERT, Text Blob, and a grammar checker, along with the Adam W optimizer, but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set the hidden size of our 2-layer Transformer encoder and decoder to be 768, and ns, nd to be 3 for TB. The size of the fixed vocabulary is 20,000, and the batch size is 512. The hyper-parameters β and γ are consistently set to 1.0 and 0.8, respectively. For training, we use Adam W (Kingma and Ba 2015) with an initial learning rate 2 × 10−5, and decrease the learning rate by a factor of 0.8 when the decrease ratio of the validation loss is smaller than 2%. |