Confidence-Aware Matrix Factorization for Recommender Systems
Authors: Chao Wang, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, Zhenya Huang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives. |
| Researcher Affiliation | Academia | Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, {wdyx2012, wrz179, hxpsola, huangzhy}@mail.ustc.edu.cn, {qiliuql, cheneh}@ustc.edu.cn Decision Science and Management Information Systems Department, Drexel University, chuanren.liu@drexel.edu |
| Pseudocode | Yes | Algorithm 1 Gibbs sampling for CBPMF |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it explicitly state that the code will be made open source. |
| Open Datasets | Yes | We conduct experiments on three real-world datasets, i.e., Movie Lens1and Netflix2 and Jester3. 1http://www.grouplens.org/node/73 2www.netflixprize.com 3http://eigentaste.berkeley.edu/dataset/ |
| Dataset Splits | No | In our experiments, we randomly select 80% to 90% data as training sets according to different datasets and the rest part as test sets for five times. The paper specifies training and test splits, but does not explicitly mention a separate validation split for hyperparameter tuning. |
| Hardware Specification | No | The paper does not specify the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In the proposed CBPMF, we introduced variance parameters γU and γV which obey Gamma distribution. As discussed in Section 3, the shape and rate of Gamma distribution is initialized via a = b so that the mean value would always be 1. Here, a and b are of great importance to statistical dispersion of Gamma distribution. Larger a and b would lead to more compact sampling results. We tune the value of a and b from the candidate set {5, 10, 20, 30, 50, 100, 200}. In PMF, following Mnih and Salakhutdinov(2008), we divide the datasets into mini-batches and update parameters after every mini-batch. In BPMF, following salakhutdinov and Mnih(2008), we set μ0 = 0 by symmetry, ν0 = D where D is the dimension of latent factors and W0 = ID D which is the identity matrix. To speed up the training process, the Gibbs sampler is initialized with PMF s output estimate. All parameters are tuned following the authors. |