Notice: 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. Full accuracy metrics and methodology are described in [1].
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Authors: Yuta Saito, Masahiro Nomura
IJCAI 2022 | Venue PDF | LLM Run Details
| 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. |