Multi-Domain Recommendation to Attract Users via Domain Preference Modeling
Authors: Hyunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, Hwanjo Yu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the effectiveness of DRIP in MDRAU task and its ability to capture users domain-level preferences. |
| Researcher Affiliation | Academia | 1 Pohang University of Science and Technology (POSTECH), Republic of Korea 2 University of Illinois at Urban-Champaign (UIUC), United States 3 Yonsei University, Republic of Korea |
| Pseudocode | No | The paper describes the DRIP architecture with a figure and text, but it does not contain structured pseudocode or an algorithm block labeled as such. |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | We use the widely-used Amazon dataset (He and Mc Auley 2016; Kang et al. 2019, 2023), which consists of multiple item domains. |
| Dataset Splits | No | The paper mentions that more detailed experimental settings are in the Appendix, but it does not explicitly provide specific train/validation/test dataset splits (e.g., exact percentages or sample counts) needed for reproduction in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'More detailed experimental settings and results are in the Appendix' but does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |