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 | Conference PDF | Archive PDF | Plain Text | 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 |