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..
Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient
Authors: Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu3312-3320
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University 2Huawei Noah s Ark Lab |
| Pseudocode | Yes | Algorithm 1 Learning TPGR |
| Open Source Code | Yes | The implementation code3 of the TPGR is available online.3https://github.com/chenhaokun/TPGR |
| Open Datasets | Yes | Movie Lens (10M).1 A dataset consists of 10 million ratings from users to movies in Movie Lens website. Netflix.2 A dataset contains 100 million ratings from Netflix s competition to improve their recommender systems. 1http://files.grouplens.org/datasets/movielens/ml-10m.zip 2https://www.kaggle.com/netflix-inc/netflix-prize-data |
| Dataset Splits | No | For each dataset, the users are randomly divided into two parts where 80% of the users are used for training while the other 20% are used for test. |
| Hardware Specification | Yes | The experiments are conducted on the same machine with 4-core 8-thread CPU (i7-4790k, 4.0GHz) and 32GB RAM. |
| Software Dependencies | No | The paper mentions software components like "fully-connected neural network", "REINFORCE algorithm", and "simple recurrent unit (SRU)" but does not provide specific version numbers for any libraries, frameworks, or solvers used. |
| Experiment Setup | Yes | In our experiments, the length of an episode is set to 32 and the trade-off factor α in the reward function is set to 0.0, 0.1 and 0.2 respectively for both datasets. For TPGR, we set the clustering tree depth d to 2 and apply the PCA-based clustering algorithm with rating-based item representation when constructing the balanced tree... all the models adopt the neural networks with the same architecture which consists of three fully-connected layers with the numbers of hidden units set to 32 and 16 respectively |