Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems

Abstract

In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.