Enhancing Long-Tail Bundle Recommendations Utilizing Composition Pattern Modeling
Authors: Tianhui Ma, Shuyao Wang, Zhi Zheng, Hui Xiong
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three public datasets demonstrate that our method can improve the performance on bundle recommendation significantly, especially on the long-tail bundles. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China 2State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China 3Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) 4Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China |
| Pseudocode | Yes | Algorithm 1 Sinkhorn-Knopp Algorithm |
| Open Source Code | Yes | The code is available at https://anonymous.4open.science/r/CALBRec-A291. |
| Open Datasets | Yes | Datasets. We selected three real-world bundle recommendation datasets that represent diverse bundle recommendation scenarios: Youshu [Chen et al., 2019b] for book bundles; Net Ease [Cao et al., 2017a] for music playlists, and i Fashion [Chen et al., 2019c] for fashion outfits. |
| Dataset Splits | Yes | Following [Ma et al., 2022; Chang et al., 2020; Deng et al., 2020], we split each dataset into training/validation/testing sets at a 7:1:2 proportion. |
| Hardware Specification | Yes | All the models were trained using Pytorch and NVIDIA Titan-V GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number. No other software dependencies are mentioned with specific version numbers. |
| Experiment Setup | Yes | For all methods, the embedding size was set as 64, Xavier normal initialization was adopted, the models were optimized using Adam optimizer with the learning rate 0.001, and the batch size was set as 2048. ... We set k to 20. ... Based on these empirical findings, we configure K as 300, 2000, and 2000 for Youshu, Net Ease and i Fashion, respectively. |