Bisociative Pivoting as a Method for Moderating User Response to Text Similarity in Next-item News Recommendation

News organizations must balance diverse journalistic exposure with user engagement, yet text similarity-based recommendation—widely deployed for privacy and cold-start advantages—remains poorly understood from a user experience perspective. We propose bisociative pivoting, which algorithmically constructs asymmetric similarity through dimensional decomposition: maintaining high similarity in one content dimension (anchor) while introducing dissimilarity in another (pivot). This creates cognitive scaffolding that may facilitate positive user responses to greater semantic distance. A user study (n=732) examining Dutch news recommendations confirms that semantic distance significantly reduces evaluation, engagement, and adoption attitude. However, bisociative pivoting substantially attenuates these penalties for item-level outcomes (36–59% reduction), though not for system-level attitude. By grounding algorithmic design in bisociation theory—how users cognitively process tension between connection and separation—we provide initial evidence that dimensional decomposition can substantially reduce the engagement costs of semantic distance. These findings suggest that bisociative pivoting warrants further investigation as an approach for managing the engagement-diversity tension in text similarity-based news recommendation.