A Review-Driven Neural Model for Sequential Recommendation
Authors: Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Engineering, Wuhan University, China 2School of Remote Sensing and Information Engineering, Wuhan University, China 3State Key Lab of Mathematical Engineering and Advanced Computing, Zhengzhou, China 4School of Computer Science, Wuhan University, China |
| Pseudocode | No | The paper describes the algorithm using text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about the release of the paper's own source code or a link to a code repository was found. |
| Open Datasets | Yes | We perform our experiments on the Amazon dataset1. This dataset contains product purchase history from Amazon ranging from May 1996 to July 2014. We conduct experiments on three categories: Instant Video (IV), Pet Supplies (PS) and Tools and Home Improvement (THI). 1http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | No | We hold the first 70% of items in each user s sequence as the training set and the rest are used for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'ReLU' but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For the proposed RNS, we set L = 5 which is a common setting in most relevant works. The number of aspects is set to be 5, and embedding size is set to 25, n = 10 and h = {1, 3, 5, 7, 9}. The learning rate is set to 0.001, x = 3, α = 0.1 and λ = 0.0001. We set N = 5 for performance evaluation. |