Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Authors: Chunxu Zhang, Guodong Long, Hongkuan Guo, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, Bo Yang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental validations on benchmark datasets demonstrate the superior performance of our proposed method. We evaluate the performance of our method on real-world industrial datasets. Experimental results show that our proposed MRFF can empower various foundation model architectures and achieve superior performance compared to state-of-the-art baselines on widely-used click-through rate (CTR) prediction task. Additionally, we conduct comprehensive experiments to analyze our model s efficacy in terms of user-grouping capability. Furthermore, we demonstrate the practical feasibility of MRFF by assessing its efficiency and privacy-preserving capabilities. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science and Technology, Jilin University, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China 3 Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney 4 Kuaishou Technology 5 Institute for AI Industry Research, Tsinghua University |
| Pseudocode | No | The paper describes the method using mathematical formulations and descriptive text, but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/Zhangcx19/AAAI-25-MRFF |
| Open Datasets | Yes | We conduct experiments on three practical datasets: Kuai Rand-Pure (Gao et al. 2022), Kuai SAR-R and Kuai SAR-S (Sun et al. 2023). |
| Dataset Splits | Yes | This paper focuses on the click-through rate (CTR) prediction task and we adopt the prevalent leave-one-out dataset split, following the setting in (Kang and Mc Auley 2018). |
| Hardware Specification | No | The paper discusses the efficiency of the model in terms of parameter size and testing time on client devices, but does not specify the hardware used for conducting the experiments. |
| Software Dependencies | No | All experiments are implemented using the Py Torch framework and repeated 5 times, with average results reported to ensure statistical reliability. |
| Experiment Setup | Yes | For two non-sequential baselines Fed NCF and Fed PA, we fix batch size as 1,024. For three transformer-based baselines and our enhanced versions, we set transformer blocks as 2 for a fair comparison. Besides, we set the total communication rounds as 500 for all models to ensure convergence. |