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..
BPAM: Recommendation Based on BP Neural Network with Attention Mechanism
Authors: Wu-Dong Xi, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, Jianhuang Lai
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model. |
| Researcher Affiliation | Academia | Wu-Dong Xi1,2 , Ling Huang1,2 , Chang-Dong Wang1,2 , Yin-Yu Zheng1 and Jianhuang Lai1 1School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China 2Guangdong Province Key Laboratory of Computational Science, Guangzhou, 510275, China EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 The algorithm framework of BPAM |
| Open Source Code | No | The paper does not provide an explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | The experiments are conducted on eight realworld publicly available datasets: Movie Lens (ml-latest (ml-la), ml-1m, ml-10m)1, filmtrust2, jester (jester-data-1 (jd-1), jester-data-2 (jd-2), jester-data-3 (jd-3))3 and Movie Tweetings (MT)4. 1https://grouplens.org/datasets/movielens/ 2https://www.librec.net/datasets.html 3http://eigentaste.berkeley.edu/dataset/ 4https://github.com/sidooms/Movie Tweetings |
| Dataset Splits | No | The paper states: 'We randomly split each dataset into the training set and testing set with ratio 3:1 for each user.' It does not explicitly mention a separate validation set split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., library names like PyTorch 1.9, TensorFlow 2.x, or specific solver versions). |
| Experiment Setup | Yes | α is the trade-off parameter which is used to tune the importance of the global weight. ... The proposed model generates the best performance with k = 5 on most of the datasets except ml-1m. On ml-1m, the best performance is achieved with k = 10. ... Additionally, we can find that the optimal attention ratio α is around 2 to 4. ... where η (0, 1) is the learning rate |