Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 14,417 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,246 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |