CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
Paper
Paper/Presentation Title | CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation |
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Presentation Type | Paper |
Authors | Qui, Ruihong, Wang, Sen, Chen, Zhi, Yin, Hongzhi and Huang, Zi |
Journal or Proceedings Title | Proceedings of the 29th ACM International Conference on Multimedia (MM ’21) |
Journal Citation | pp. 3844-3852 |
Number of Pages | 9 |
Year | 2021 |
Publisher | Association for Computing Machinery (ACM) |
Place of Publication | United States |
ISBN | 9781450386517 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3474085.3475266 |
Web Address (URL) of Paper | https://dl.acm.org/doi/abs/10.1145/3474085.3475266 |
Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3474085 |
Conference/Event | 29th ACM International Conference on Multimedia (MM '21) |
Event Details | 29th ACM International Conference on Multimedia (MM '21) Parent ACM International Conference on Multimedia Delivery Online Event Date 20 to end of 24 Oct 2021 |
Abstract | Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference for these items in addition to the historical user-item interaction records. 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. |
Keywords | visually-aware; causal inference; debiased recommendation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
Public Notes | © 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MM '21: Proceedings of the 29th ACM International Conference on Multimedia, https://doi.org/10.1145/3474085.3475266 |
Byline Affiliations | University of Queensland |
https://research.usq.edu.au/item/zyx20/causalrec-causal-inference-for-visual-debiasing-in-visually-aware-recommendation
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