Fuzzy Collaborative Reasoning
Authors: Huanhuan Yuan, Pengpeng Zhao, Jiaqing Fan, Junhua Fang, Guanfeng Liu, Victor S. Sheng
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on publicly available datasets demonstrate the superiority of this method in solving the sequential recommendation task. [...] Experiments In this section, we provide the details about experimental settings and results. [...] Overall Performance The experimental results on three public datasets are shown in Table 2. |
| Researcher Affiliation | Academia | Huanhuan Yuan1,2, Pengpeng Zhao1*, Jiaqing Fan1, Junhua Fang1, Guanfeng Liu2, Victor S. Sheng3 1Soochow University, 2Macquarie University, 3Texas Tech University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and processes verbally and with a computation graph (Figure 1), but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | ML100k (Harper and Konstan 2016): Movie Lens is one of the most widely used datasets for recommendation. It includes 100,000 ratings provided by 943 users. Amazon (He and Mc Auley 2016): The Amazon dataset collection includes various e-commerce datasets crawled from the Amazon website. In our experiment, we select two relatively sparse subsets, Beauty and Sports, from the Amazon dataset for a comparative analysis. |
| Dataset Splits | Yes | The last two positive interactions for each user are assigned to the validation set and test set, respectively, with the remaining historical interactions used for training. |
| Hardware Specification | Yes | We conduct our implementation of all methods by using Py Torch (Paszke et al. 2017) with the Adam optimizer (Kingma and Ba 2015) on a 32GB Tesla V100-PCIE GPU. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al. 2017)" and "Adam optimizer (Kingma and Ba 2015)" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | All models are trained for 100 epochs with a learning rate of 0.001, applying early stopping after 20 epochs. The L2 regularization weight λ is set to 0.0001. Unless specified otherwise, the sequence length is configured to 5. We use a batch size of 256 and set the embedding dimension to 64. The coefficient γcoff is adjusted to 15 for the Sports dataset and 25 for the other two datasets. |