Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Authors: Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen3470-3477
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on realworld datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm f BGD.In this section, we conduct experiments to evaluate the performance of FAWMF. |
| Researcher Affiliation | Collaboration | Jiawei Chen,1,2 Can Wang,1,2, Sheng Zhou,1 Qihao Shi,1 Jingbang Chen,1 Yan Feng,1,2 Chun Chen1 1College of Computer Science, Zhejiang University, China 2Zhejiang University-Lianlian Pay Joint Research Center |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. No repository link or explicit code release statement is found. |
| Open Datasets | Yes | Three benchmark datasets Moivelens 2, Amazon (food-reviews) 3, Douban 4 are used in our experiments. 2https://grouplens.org/datasets/movielens/ 3https://www.kaggle.com/snap/amazon-fine-food-reviews 4https://www.cse.cuhk.edu.cn/irwin.king.new/pub/data/douban |
| Dataset Splits | Yes | Grid search and 5fold cross validation are used to find the best parameters. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In our FAWMF, we set D=K=20, ε=1e-5 and learning rate=0.1 across all datasets. |