2026
Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure
AAAI 2026technical
Beyond user-item modeling, item-to-item relationships are increasingly used to enhance recommendation. However, common methods largely rely on co-occurrence, making them prone to item popularity bias and user attributes, which degrades embedding quality and performance. Meanwhile, although diversity