Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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-ο¬ne-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 ο¬nd 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, Ξ΅=1e-5 and learning rate=0.1 across all datasets. |