mlsanityData to 7 Oct 2026

Exploring and Exploiting Data Heterogeneity in Recommendation

arXiv 2305.15431 ↗First posted 21 May 2023cs.IR cs.LGcited by 1

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Abstract (arXiv)

Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation systems. It reflects the differences in the properties among sub-populations. Ignoring the heterogeneity in recommendation data could limit the performance of recommendation models, hurt the sub-populational robustness, and make the models misled by biases. However, data heterogeneity has not attracted substantial attention in the recommendation community. Therefore, it inspires us to adequately explore and exploit heterogeneity for solving the above problems and assisting data analysis. In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method. Furthermore, the uncovered heterogeneity is exploited for two purposes in recommendation scenarios which are prediction with multiple sub-models and supporting debias. Extensive experiments on real-world data validate the existence of heterogeneity in recommendation data and the effectiveness of exploring and exploiting data heterogeneity in recommendation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 75 references that are in the map. First posted on arXiv 21 May 2023.

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36 of 75 references.

References · 36

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24 more
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  1. 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendationscited by 2