We lifted 3 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| RuihongQiu/cornac | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| ranking_eval | Not yet run | RuihongQiu/cornac/cornac/eval_methods/base_method.py code served (permissive licence) · get_code("f8d37f2574e0943c") |
| ranking_eval | Not yet run | RuihongQiu/cornac/cornac/eval_methods/propensity_stratified_evaluation.py code served (permissive licence) · get_code("847d6051904ca2f1") |
| rating_eval | Not yet run | RuihongQiu/cornac/cornac/eval_methods/base_method.py code served (permissive licence) · get_code("7e814184a3613916") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference. It is commonly observed that user's attention to visual features does not always reflect the real preference. Although a user may click and view an item in light of a visual satisfaction of their expectations, a real purchase does not always occur due to the unsatisfaction of other essential features (e.g., brand, material, price). We refer to the reason for such a visually related interaction deviating from the real preference as a visual bias. Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation. In this paper, we derive a causal graph to identify and analyze the visual bias of these existing methods. In this causal graph, the visual feature of an item acts as a mediator, which could introduce a spurious relationship between the user and the item. To eliminate this spurious relationship that misleads the prediction of the user's real preference, an intervention and a counterfactual inference are developed over the mediator. Particularly, the Total Indirect Effect is applied for a debiased prediction during the testing phase of the model. This causal inference framework is model agnostic such that it can be integrated into the existing methods. Furthermore, we propose a debiased visually-aware recommender system, denoted as CausalRec to effectively retain the supportive significance of the visual information and remove the visual bias. Extensive experiments are conducted on eight benchmark datasets, which shows the state-of-the-art performance of CausalRec and the efficacy of debiasing.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.02390")
get_code_for_paper("2107.02390")
have("2107.02390")
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