Collaborative Rating Allocation
Authors: Yali Du, Chang Xu, Dacheng Tao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world data sets demonstrate our model s competitiveness versus other collaborative rating prediction methods.In this section, we evaluate the proposed MC-S on three real-world datasets and compare it with PMF [Salakhutdinov and Mnih, 2007], NPCA [Yu et al., 2009], LGeom CG [Vandereycken, 2013] and LMa Fit [Wen et al., 2012]. |
| Researcher Affiliation | Academia | Yali Du , Chang Xu , Dacheng Tao Center for Artificial Intelligence, FEIT, University of Technology Sydney UBTech Sydney AI Institute, The School of IT, FEIT, The University of Sydney yali.du@student.uts.edu.au, {c.xu, dacheng.tao}@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 Alternating Direction Method for Eq. (3) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | Yes | We next explore the proposed MC-S s performance on Movie Lens dataset in collaborative filtering [Harper and Konstan, 2015]. |
| Dataset Splits | Yes | Then we split the dataset into training, validation and test sets. The test and validation sets are created by reserving one rating from each user respectively. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'vlfeat toolbox' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Define sampling fraction p = |Ω| (n+1)m as the ratio of observed entries to total entries. By setting p = 0.5, we randomly keep 50% of features for fitting collaborative filtering models. Then we split the dataset into training, validation and test sets. The number of basis points on the simplex r is set to be the number of classes. |