Towards Dynamic Relationship Schema Discovery for Complementary News Recommendation

Abstract

Complementary relationships are commonly used to represent cross-item dependencies in recommender systems such as e-commerce, where one item functionally complements another. This concept also applies to news recommendation, where complementary relationships capture likely follow-up articles that users read after a given article, such as background explainers or timeline updates. Existing approaches often do not explicitly model document-level complementary relationships and typically rely on fixed schemas, limiting adaptation to evolving news-consumption patterns and potentially reducing recommendation quality and interpretability. This study proposes Dynamic Relationship Schema Discovery (DRSD), an iterative framework that identifies schema-gap article pairs, uses an LLM to propose and annotate new relationship labels, retrains a relationship-conditioned CTR model, and prunes labels that fail to improve validation performance. Preliminary experiments show that DRSD increases Catalog Coverage and filters weak relationship hypotheses through validation-driven pruning while maintaining competitive recommendation quality. These findings suggest that DRSD is a promising direction for refining complementary relationship schemas in news recommendation.