Active Large Language Model-Based Knowledge Distillation for Session-Based Recommendation
Authors: Yingpeng Du, Zhu Sun, Ziyan Wang, Haoyan Chua, Jie Zhang, Yew-Soon Ong
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets show that our method significantly outperforms state-of-the-art methods for SBR. We conduct extensive experiments to evaluate the performance of ALKDRec and answer four research questions. |
| Researcher Affiliation | Academia | 1College of Computing and Data Science, Nanyang Technological University, Singapore 2Information Systems Technology and Design, Singapore University of Technology and Design, Singapore 3A*STAR Center for Frontier AI Research, Singapore EMAIL,zhu EMAIL,EMAIL, EMAIL,EMAIL,EMAIL |
| Pseudocode | No | The paper describes the proposed method conceptually and mathematically, including definitions, theorems, and equations, but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | 1Due to space limitations, we only provide the main sketch of proofs, while the detailed proofs can be found in Appendix B of our Git Hub repository at https://github.com/kk97111/ALKDRec. |
| Open Datasets | Yes | Datasets. We evaluate ALKDRec and baselines on two real-world datasets, namely Hetrec2011-ML and Amazon Games. |
| Dataset Splits | Yes | We randomly split sessions into training, validation, and test sets by 6:2:2. For evaluation phase, we adopt the widely used leave-oneout evaluation protocol. |
| Hardware Specification | No | No specific hardware details for running experiments are provided. The paper mentions using the GPT-4-turbo API and associated costs ('e.g., around 44 minutes and 8.6 USD for Chat GPT API in Amazon-Games'), but not the underlying hardware specifications for the experimental environment. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and 'GPT-4-turbo2024-04-09' as the LLM teacher model, but does not provide specific version numbers for general software dependencies like programming languages or libraries used for implementation. |
| Experiment Setup | Yes | For an effective instance... we set µ = 10 empirically. For similar and incorrect instances, we assign them with lower gain values compared to effective instances, i.e., gsi s = gin s = gef s /2. We adopt the GPT-4-turbo2024-04-09 as the LLM teacher to distill knowledge from 500 instances... We set the number of effective/similar/incorrect instances as 1:5:4... For αv in Equation (1), we assign 3/2/1... we set the latent dimensions for the teacher and student recommenders at 100 and 10... We set the learning rate as 1 10 3 with Adam optimizer and batch size as 1024 for all methods. |