Be causal: De-biasing social network confounding in recommendation

Qian LI, Xiangmeng WANG, Zhichao WANG, Guandong XU

Research output: Contribution to journalArticlespeer-review

12 Citations (Scopus)


In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called "exposure"perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC (De-Bias Network Confounding in Recommendation), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network's perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines. Copyright © 2023 Association for Computing Machinery.

Original languageEnglish
Article number14
JournalACM Transactions on Knowledge Discovery from Data
Issue number1
Publication statusPublished - Feb 2023


Li, Q., Wang, X., Wang, Z., & Xu, G. (2023). Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data, 17(1), Article 14.


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