Doubly Calibrated Estimator for Recommendation on Data Missing Not at Random

Kweon, Wonbin; Yu, Hwanjo

doi:10.1145/3589334.3645617

Proceedings of the ACM Web Conference 2024 2024

DOI: 10.1145/3589334.3645617

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Doubly Calibrated Estimator for Recommendation on Data Missing Not at Random

Wonbin Kweon,

Hwanjo Yu

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Cited by 2 publications

References 22 publications

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Uncovering the Propensity Identification Problem in Debiased Recommendations

Zhang,

Wang,

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Uncovering the Propensity Identification Problem in Debiased Recommendations

Zhang,

Wang,

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Top-Personalized-K Recommendation

Kweon,

Kang,

Jang

et al. 2024

Proceedings of the ACM Web Conference 2024

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The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is this fixed-size top-𝐾 recommendation the optimal approach for every user's satisfaction? Not necessarily. We point out that providing fixed-size recommendations without taking into account user utility can be suboptimal, as it may unavoidably include irrelevant items or limit the exposure to relevant ones. To address this issue, we introduce Top-Personalized-𝐾 Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction. As a solution to the proposed task, we develop a model-agnostic framework named PerK. PerK estimates the expected user utility by leveraging calibrated interaction probabilities, subsequently selecting the recommendation size that maximizes this expected utility. Through extensive experiments on real-world datasets, we demonstrate the superiority of PerK in Top-Personalized-𝐾 recommendation task. We expect that Top-Personalized-𝐾 recommendation has the potential to offer enhanced solutions for various real-world recommendation scenarios, based on its great compatibility with existing models.

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Doubly Calibrated Estimator for Recommendation on Data Missing Not at Random

Cited by 2 publications

References 22 publications

Uncovering the Propensity Identification Problem in Debiased Recommendations

Uncovering the Propensity Identification Problem in Debiased Recommendations

Top-Personalized-K Recommendation

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