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..
Cross-Domain Recommendation: An Embedding and Mapping Approach
Authors: Tong Man, Huawei Shen, Xiaolong Jin, Xueqi Cheng
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms stateof-the-art cross-domain recommendation methods. |
| Researcher Affiliation | Academia | {CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, China} 2{University of Chinese Academy of Sciences, China} |
| Pseudocode | Yes | Algorithm 1 The EMCDR framework. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | Two real-world datasets are adopted for evaluation. The first dataset, Movie Lens-Netflix... The second dataset was crawled from an online social network, i.e., Douban [Huang et al., 2012], where users give rating to books and movies |
| Dataset Splits | Yes | The learning rate and regularization coefficients are optimized via 5-fold cross validation on the training dataset. |
| Hardware Specification | No | The paper does not specify any hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using a 'tan-sigmoid function' as activation and 'stochastic gradient descent' for optimization, but does not list any specific software libraries or their version numbers. |
| Experiment Setup | Yes | Dimension K of the latent factor is set as 20, 50, and 100. The learning rate and regularization coefficients are optimized via 5-fold cross validation on the training dataset. For the linear mapping function, the size of the permutation matrix is K K, and the regularization coefficient λM is chosen as 0.01; for the MLP mapping function, we choose the structure of the MLP as one-hidden layer, the dimension of the input and output of the MLP is set as K, whilst the number of nodes in the hidden layer is set as 2 K. The weight and bias parameters of the MLP is initialized according to the rule in [Glorot and Bengio, 2010]. We use minibatch with a size of 16 and no momentum is used. Finally, a tan-sigmoid function is employed as the activation function. |