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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation
Authors: Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Xiaolin Zheng
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1 Department of Computing, Macquarie University, Sydney, NSW 2109, Australia 2 Ant Financial Services Group, Hangzhou 310012, China 3 College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of its source code. |
| Open Datasets | Yes | To validate the recommendation performance of our GA-DTCDR approach and baseline approaches, we choose four real-world datasets (see Table 2), i.e., three Douban subsets (Douban Book, Douban Music, and Douban Movie) [Zhu et al., 2019], and Movie Lens 20M [Harper and Konstan, 2016]. |
| Dataset Splits | No | The paper describes its test split and training strategy but does not explicitly mention a validation split or how validation was performed for hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions software like Doc2vec, Node2vec, Adam, and Stanford Core NLP, but it does not specify their version numbers. |
| Experiment Setup | Yes | For training our GA-DTCDR, we randomly select 7 negative instances for each observed positive instance into Y sampled, adopt Adam [Kingma and Ba, 2014] to train the neural network, and set the maximum number of training epochs to 50. The learning rate is 0.001, the regularization coefficient λ is 0.001, and the batch size is 1, 024. To answer Q3, the dimension k of the embedding varies in {8, 16, 32, 64, 128}. |