REMIT: Reinforced Multi-Interest Transfer for Cross-Domain Recommendation
Authors: Caiqi Sun, Jiewei Gu, Binbin Hu, Xin Dong, Hai Li, Lei Cheng, Linjian Mo
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results on large real-world datasets demonstrate the superior performance and compatibility of REMIT. |
| Researcher Affiliation | Collaboration | 1 Ant Group 2 School of Data Science, Fudan University {caiqi.scq, gujiewei.gjw, bin.hbb, zhaoxin.dx, tianshu.lh, lei.chenglei, linyi01}@antgroup.com |
| Pseudocode | Yes | Algorithm 1: Overall Training Procedure of REMIT |
| Open Source Code | Yes | REMIT 2Code is available at https://github.com/mufusu21/REMIT |
| Open Datasets | Yes | Following most existing methods (Zhu et al. 2021, 2022), we test our algorithms on the Amazon review dataset1. Specifically, we pick 3 datasets out of 24, i.e., movies and tv (Movie), cds and vinyl (Music), and books (Book). Three cross-domain recommendation tasks are built upon these three datasets: Movie Music (Task 1), Book Movie (Task 2) and Book Movie (Task 3). ... 1http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | No | The paper describes how test users are defined by percentages (80%, 50%, 20%) and that other overlapping users are used for training, but it does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper only states "built based on the code repository of PTUPCDR using Py Torch and GPU" without specifying exact hardware details like GPU model, CPU, or memory. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not provide a specific version number or other software dependencies with their versions. |
| Experiment Setup | Yes | For both MIT and IS in each task and method, the initial learning rate for the Adam (Kingma and Ba 2014) optimizer is tuned by grid searches within {0.001, 0.005, 0.01, 0.02, 0.1}. The epoch number is tuned by grid searchs between 5 and 15. In addition, we set the dimension of embeddings as 10 and the batch size as 512. The meta-path set is {uiu, uiciu, uibiu}, which represents users interests of item, category and brand. The hidden size of meta network in MIT and policy network in IS is set to 50 and 25. |