Flow Matching for Denoised Social Recommendation
Authors: Yinxuan Huang, Ke Liang, Zhuofan Dong, Xiaodong Qu, Wang Tianxiang, Yue Han, Jingao Xu, Bin Zhou, Ye Wang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present a series of experiments conducted to evaluate the performance of our Rec Flow method, focusing on the following five questions: Q1: How does Rec Flow perform in comparison to other state-of-the-art social recommendation methods? Q2: What are the key contributions of Rec Flow s main modules? Q3: Is Rec Flow robust enough to effectively handle noisy and sparse data in social recommendation (SR)? Q4: How do different settings impact the performance of Rec Flow? Q5: How does the complexity of our method compare to that of alternative approaches? |
| Researcher Affiliation | Academia | 1School of Computer Science, National University of Defense Technology, Changsha, Hunan, China 2University of Chicago, Chicago, Illinois, United States 3Harbin Institute of Technology(Shenzhen), Shenzhen, Guangdong, China. |
| Pseudocode | Yes | Algorithm 1 Rec Flow Training Input: Users social interaction embedding Es Output: The reconstructed embedding which is denoted as ˆe0 Algorithm 2 Rec Flow Inference Input: Users interaction vectors xu, u = 1, 2, . . . , |U|; optimized parameter θ Output: Predicted user embeddings or interaction outcomes |
| Open Source Code | No | Code are available at . |
| Open Datasets | Yes | We conducted experiments on three publicly available social recommendation datasets: Ciao, Yelp and Epinions. Detailed statistics for these datasets are provided in Table 2. Our datasets are obtained from Rec Diff (Li et al., 2024a)1 Datasets are available at https://github.com/HKUDS/Rec Diff. |
| Dataset Splits | Yes | We applied a 7:1:2 ratio to split each dataset into training, validation, and test sets, adhering to common data partitioning practices in graph-based recommendation systems. |
| Hardware Specification | Yes | All experiments are conducted on a machine with an RTX A800 for a fair comparison. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It mentions concepts like GELU activation but not a software library and its version. |
| Experiment Setup | Yes | The learning rate was tuned within [5e 4, 1e 3, 5e 3] with a 0.96 decay factor per epoch. Batch sizes were selected from [1024, 2048, 4096, 8192], and hidden dimensions from [64, 128, 256, 512]. The parameter γ was set according to the γpctpercentile of node embedding distances for each dataset. The optimal number of GNN layers was chosen from [1, 2, 3, 4]. The Timestep embedding size is selected from 4,8,16,32. And the batch size for Ciao is 2048, while for Yelp and Epinions is 4096. Regularization weights λ1 were selected from [1e 3, 1e 2, 1e 1, 1e0, 1e1]. |