We lifted 12 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| usaito/ijcai2022-adversarial-mf | canonical | 10 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| aoa_evaluator | Ran | usaito/ijcai2022-adversarial-mf/src/evaluate/evaluator.py code served (permissive licence) · get_code("9d896727666eebe0") |
| create_obs_matrix | Ran | usaito/ijcai2022-adversarial-mf/src/utils/train_helpers.py code served (permissive licence) · get_code("79ff3156f7596d4b") |
| grad_std_logistic_function | Ran | usaito/ijcai2022-adversarial-mf/src/models/onebit_mc.py code served (permissive licence) · get_code("a4405fc7c76693fc") |
| kl_div | Ran | usaito/ijcai2022-adversarial-mf/src/utils/result_tools.py code served (permissive licence) · get_code("249c7ddf79d8e8cc") |
| mod_logistic_function | Ran | usaito/ijcai2022-adversarial-mf/src/models/onebit_mc.py code served (permissive licence) · get_code("fecf61da531f26e3") |
| ndcg_at_k | Ran | usaito/ijcai2022-adversarial-mf/src/evaluate/evaluator.py code served (permissive licence) · get_code("fee1fe126261da74") |
| preprocess_yahoo_coat | Ran | usaito/ijcai2022-adversarial-mf/src/utils/preprocessor.py code served (permissive licence) · get_code("0cad47f808030dbd") |
| recall_at_k | Ran | usaito/ijcai2022-adversarial-mf/src/evaluate/evaluator.py code served (permissive licence) · get_code("c2d33d7739234595") |
| std_logistic_function | Ran | usaito/ijcai2022-adversarial-mf/src/models/onebit_mc.py code served (permissive licence) · get_code("2eb6d89deb934a20") |
| transform_model_name | Ran | usaito/ijcai2022-adversarial-mf/src/utils/result_tools.py code served (permissive licence) · get_code("9e8299a56ae1a3d6") |
| create_unlabeled_mcar_data | Not yet run | usaito/ijcai2022-adversarial-mf/src/utils/train_helpers.py code served (permissive licence) · get_code("35a78c04e9fe6677") |
| identify_model_names | Not yet run | usaito/ijcai2022-adversarial-mf/src/utils/result_tools.py code served (permissive licence) · get_code("b4a1b91b4ce4dceb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1910.07295")
get_code_for_paper("1910.07295")
have("1910.07295")
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