OrDA: Orthogonal Disentanglement of Access Habits Framework for Homepage Marketing Block Recommendations
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Computer Science > Machine Learning
Title:OrDA: Orthogonal Disentanglement of Access Habits Framework for Homepage Marketing Block Recommendations
Abstract:Clicks on homepage marketing blocks are driven by a dual-mechanism of content interest and access habits. However, habitual clicks often create Pseudo-Positives in marketing slots, where position advantage masks mediocre content quality, leading to biased recommendation ecosystems. We propose a framework called Orthogonal Disentanglement of Access habits (OrDA) to purify interest signals. OrDA utilizes a dual-tower structure with a gated allocation layer to adaptively route features and minimize interference. To ensure rigorous separation, we employ orthogonal regularization to constrain the latent interest and habit manifolds to be geometrically perpendicular. OrDA performs causal intervention (do-calculus) during inference to rank items solely by purified interest scores. Empirical online evaluations on large-scale datasets demonstrate that OrDA effectively eliminates access-habit bias, outperforming state-of-the-art methods in predictive accuracy. Online AB test 5.64% shows user click-through rates (UCTR) improvement on the Zhima homepage marketing block, Zhima rent-floor recommendation.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.13420 [cs.LG] |
| (or arXiv:2607.13420v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13420
arXiv-issued DOI via DataCite (pending registration)
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