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..
Learning Disentangled Representations for Recommendation
Authors: Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We conduct our experiments on five real-world datasets. |
| Researcher Affiliation | Collaboration | Jianxin Ma1,2 , Chang Zhou1 , Peng Cui2, Hongxia Yang1, Wenwu Zhu2 1Alibaba Group, 2Tsinghua University |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The dataset and our code are at https://jianxinma.github.io/disentangle-recsys.html. |
| Open Datasets | Yes | We conduct our experiments on five real-world datasets. Specifically, we use the largescale Netflix Prize dataset [4], and three Movie Lens datasets of different scales (i.e., ML-100k, ML-1M, and ML-20M) [16]. We additionally collect a dataset, named Ali Shop-7C 2, from Alibaba s e-commerce platform Taobao. 2The dataset and our code are at https://jianxinma.github.io/disentangle-recsys.html. |
| Dataset Splits | No | The paper states 'We follow the experiment protocol established by the previous work [32] strictly, and use the same preprocessing procedure as well as evaluation metrics.' but does not explicitly provide the specific train/validation/test dataset splits (e.g., percentages or counts) within the paper itself. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or specific computing environments used for experiments. |
| Software Dependencies | No | The paper mentions 'Adam' as an optimizer and 'Hyepropt' for hyperparameter tuning, but no specific version numbers are provided for these or any other software dependencies. |
| Experiment Setup | Yes | We constrain the number of learnable parameters to be around 2Md for each method so as to ensure fair comparison... We set d = 100 unless otherwise specified. We fix τ to 0.1. We tune the other hyper-parameters of both our approach s and our baselines automatically using the TPE method [6] implemented by Hyepropt [5]. |