Abstract
In recommender systems, learning representations of users and items is a crucial task for predicting user preferences. However, observational data suffer from inherent bias problems. Various confounding factors are present and lead to data biases, which result in incomplete and skewed representations and make it difficult to accurately determine a user’s true preference. Recent studies have utilized unbiased data to alleviate the bias problem, but these methods do not learn about the representations of user interests, which are not affected by confounding factors. To address this gap, we propose a general disentangled framework, named DCRL, to learn causal representations for obtaining unbiased recommendations. We first analyze the interaction process in a recommender system based on causal graphs and propose that disentanglement can be achieved through intervening embeddings. DCRL leverages unbiased data as supervision signals to guide the disentanglement process, enabling causal representations to learn unbiased features and eliminate the effects of confounding factors. This approach is a model-agnostic solution because disentanglement is an additional task that can be implemented on basic recommendation models. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of DCRL in comparison with the state-of-the-art baselines.
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Acknowledgements
This work was supported by the National Key Research and Development Program of China (2019YFB2102500) and the National Natural Science Foundation of China (U2268203).
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Yang, X., Li, X., Liu, Z. et al. Disentangled causal representation learning for debiasing recommendation with uniform data. Appl Intell 54, 6760–6775 (2024). https://doi.org/10.1007/s10489-024-05497-9
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DOI: https://doi.org/10.1007/s10489-024-05497-9