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.
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Authors: Yuta Saito, Masahiro Nomura
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 15,701 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,948 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 experiments on real-world datasets demonstrate that the proposed algorithm is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data. |
| Researcher Affiliation | Collaboration | Yuta Saito1 , Masahiro Nomura2 1Cornell University 2Cyber Agent, Inc. EMAIL, nomura EMAIL |
| Pseudocode | Yes | Algorithm 1 Domain Adversarial Matrix Factorization |
| Open Source Code | Yes | Our code is available at https://github.com/usaito/ijcai2022-adversarial-mf. |
| Open Datasets | Yes | Datasets. We use Yahoo! R33 and Coat4, which separately contain MNAR and MCAR datasets. 3http://webscope.sandbox.yahoo.com/ 4https://www.cs.cornell.edu/~schnabts/mnar/ |
| Dataset Splits | Yes | We randomly selected 10% of the MNAR training set as the validation set, which is used to perform hyper-parameter tuning. |
| Hardware Specification | No | The paper describes experimental setups and training details but does not provide specific hardware specifications such as GPU or CPU models. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Appendix B describes detailed experimental settings such as dataset description, evaluation metrics, and hyper-parameter tuning. |