Multidimensional Weighted Ranking Recommendation System for Circular Material Matchmaking

Abstract: Circular construction relies on digital marketplaces to overcome supply chain fragmentation, yet manual search for reclaimed components remains a critical bottleneck. This research develops a context-aware recommendation system for reusable material matchmaking. Utilizing a multidimensional weighted ranking algorithm with hard and soft constraints, the system adaptively aligns material attributes with project requirements. Validated through expert elicitation and a design simulation user study, results demonstrate high material findability and low cognitive load. However, insufficient algorithmic transparency can induce user friction. This work establishes a foundation for role-centric matchmaking, with future research focusing on similarity score cutoffs, enhanced interpretability, and API-driven cross-platform interoperability.