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..
Constrained Preference Embedding for Item Recommendation
Authors: Xin Wang, Congfu Xu, Yunhui Guo, Hui Qian
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we will test CPE and the proposed algorithms, and prove their effectiveness. |
| Researcher Affiliation | Academia | Xin Wang, Congfu Xu , Yunhui Guo, Hui Qian College of Computer Science and Technology, Zhejiang University, China EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The first 5 datasets are from Amazon.com[Mc Auley and Leskovec, 2013]; the movies, books and music datasets are from Dou Ban.com[Wang et al., 2014a]. |
| Dataset Splits | Yes | Each dataset is subdivided into three parts; 80% of it is used for training, 10% is used for validation and the last 10% is left for test. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, or cloud computing instance specifications). |
| Software Dependencies | No | The paper mentions using "stochastic gradient descent (SGD)" but does not specify any software libraries, frameworks, or their version numbers used for implementation. |
| Experiment Setup | Yes | For all the approaches, the learning rate is set to 0.05, and the latent dimension is set to 10 (i.e., d = 10). The regularization coefficient is selected from {1, 0.1, 0.01, 0.001}; t1 = t2 = 2; Because the adopted weighted sampling method for SGD, we set e, wfp,fq and wf to 1. For GBPR-MF, the group size is 3. |