MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation
Authors: Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, Chi Xu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms. |
| Researcher Affiliation | Academia | 1Nanyang Technological University, Singapore 2Delft University of Technology, The Netherlands 3 Singapore Institute of Manufacturing Technology, Singapore |
| Pseudocode | Yes | Algorithm 1: The optimization of MRLR |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We adopt the Amazon Web store data [Mc Auley et al., 2015], which contains a series of datasets from various domains (e.g., clothing, electronics). To evaluate the effectiveness of MRLR, we choose four datasets, including Clothing, Electronics, Sports, Home. |
| Dataset Splits | Yes | Standard 5-fold cross validation is adopted to evaluate all the methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameter Settings. We empirically find out the optimal parameter settings for all method. We set d = 10. We apply a grid search in {0.001, 0.01, 0.1, 1.0} for the learning rate γ, λΘ and 1/2-way regularization of FM, and a grid search in {1, 5, 10, 20, 50} for the number of negative instances N. |