Constrained Preference Embedding for Item Recommendation
Authors: Xin Wang, Congfu Xu, Yunhui Guo, Hui Qian
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 {cswangxinm, xucongfu, gyhui, qianhui}@zju.edu.cn |
| 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. |