Exploring and Exploiting Data Heterogeneity in Recommendation

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 mislead the models, hurt the sub-populational robustness, and finally limit the performance of recommendation models. However, data heterogeneity has not received substantial attention within the recommendation community, prompting us to adequately explore and exploit data heterogeneity to solve these challenges and enhance data analysis. In this study, we specifically focus on two representative categories of heterogeneity in recommendation data: heterogeneity of prediction mechanism and covariate distribution. To explore the data heterogeneity, we propose an algorithm based on bilevel clustering. Additionally, we demonstrate how the explored data heterogeneity can be exploited for prediction and debias in recommendation scenarios, specifically by building models using multiple sub-models and augmenting the propensity score estimation. Extensive experiments conducted on real-world data substantiate the existence of heterogeneity in recommendation data and validate the effectiveness of exploring and exploiting data heterogeneity in improving recommendation performance.