Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 16,814 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,756 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 ο¬nd 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 Ξ³, λΠand 1/2-way regularization of FM, and a grid search in {1, 5, 10, 20, 50} for the number of negative instances N. |