Bayesian Deep Collaborative Matrix Factorization
Authors: Teng Xiao, Shangsong Liang, Weizhou Shen, Zaiqiao Meng5474-5481
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on two sparse datasets show that BDCMF can significantly outperform the state-of-the-art CF methods. and Experiments Experimental Setup Research Questions. The research questions guiding the remainder of the paper are: (RQ1) How does our proposed BDCMF-1 compare to state-of-the-art MF methods for CF on sparsity matrix? (RQ2) How do different parameter settings (e.g., the social parameter λq and content parameter λv) affect BDCMF-1? (RQ3) Does the Bayesian posterior estimation outperform Bayesian point estimation? Datasets. In order to answer our research questions, we conduct experiments on two real-world datasets from Lastfm 1 (lastfm-2k) and Delicious2 (delicious-2k) collected by Brusilovsky et al. (2010). |
| Researcher Affiliation | Academia | Teng Xiao,1,2 Shangsong Liang,1,2, Weizhou Shen,1,2 Zaiqiao Meng1,2 1School of Data and Computer Science, Sun Yat-sen University, China 2Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China {cstengxiao, liangshangsong}@gmail.com, shenwzh3@mail2.sysu.edu.cn, zqmeng@aliyun.com |
| Pseudocode | Yes | Algorithm 1 BDCMF-1 inference algorithm. Require: user-item matrix R, item content matrix X, parameters λv, λu, λg, and λq, and P = 100 and L = 1. 1: Randomly initialize variational parameters ˆui, ˆvj, ˆgk and network parameters θ and φ. 2: while not converged do... |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Datasets. In order to answer our research questions, we conduct experiments on two real-world datasets from Lastfm 1 (lastfm-2k) and Delicious2 (delicious-2k) collected by Brusilovsky et al. (2010). and 1http://www.lastfm.com. 2http://www.delicious.com. |
| Dataset Splits | No | To evaluate our model performance on extreme sparse matrix, we use 90% of dataset to train our model and the remainder for testing. For fair comparisons, We first set the parameters for PMF, So Rec, CTR, CTR-SMF, CDL, Neu MF via five-fold cross validation. The paper specifies a 90% training split and "the remainder for testing", but does not explicitly define a separate validation set split for their own model. While it mentions 5-fold cross-validation for baselines, it doesn't specify a validation split for their primary model's reproduction. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU/CPU models or other computing specifications. |
| Software Dependencies | No | The paper mentions general software concepts like neural networks but does not provide specific version numbers for any programming languages, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Settings. For fair comparisons, We first set the parameters for PMF, So Rec, CTR, CTR-SMF, CDL, Neu MF via five-fold cross validation. For PMF, we set D, λu and λv as 50, 0.01 and 0.001, respectively. The parameter settings for So Rec are λc = 10, λu = λv = λz = 0.001. For CTR, we find it achieve best performance when D=50, λu=0.1, λv=100, a=1 and b=0.01. CTR-SMF yields the best performance when D=75, λu=0.1, λv=100, λq=100, a=1 and b=0.01. For CDL, it achieves the best performance when we set a = 1, b = 0.01, D = 50, λu=1, λv=10, λn = 1000, and λw=0.0001. For Neu MF, we set D=50, and the last hidden layer is 16. For Poisson MF-CS, we set λc=0.1 and λs=0.1. For our model BDCMF, we set D=50. We set λv=1, λq=10 for dataset Lastfm, and set λv=0.1, λq=10 for dataset Delicious. ... The dimensions of the layers of the network for BDCMF are (L+N)-200-100(zj)-100-100-(L+N), where L and N are the dimension of xj and R j. |