## 1 Introduction  
Recent research in decision-making has explored new directions of natural language usually, which has given rise to the emergence of a new perspective on information aggregation that can be termed 'human-centric aggregation'. The primary objective of this paper is two-fold. First, to incorporate the concept of human-centric aggregation via Ordered Weighted Aggregation (OWA), which was recently presented by [PERSON], [PERSON], and [PERSON] in the memorial issue of the reputed IEEE Computational Intelligence magazine [1]. Second, to design a recommender system that can assimilate ranking to the voters or rankers and assign them weights accordingly, which in turn may produce recommendations where experts' suggestions are given priority over usual recommendations [2]. It is interesting to explore the ways in which human-centric aggregation can be useful in solving real-life problems. By human-centric aggregation, we mean quantifying those qualitative attributes that are human-specific such as judgment, intelligence, intention, vision, etc. OWA has been extensively used in the literature to explore different aspects of human-specific problems [3; 4; 5; 6; 7; 8], especially in decision-making problems. In this paper, we try to explore its diversity and strength for recommender systems. Although OWA has been used in the context of recommendations in the past [9; 10], we have modified the weight assignment method that is used. The new method for assigning weights gives more weight to the best ranked to improve recommendation quality. We use the'most preferred first' linguistic quantifier with OWA for this purpose. In this paper, our aim is to recommend top books to university students by aggregating the suggestions of experts from top-ranked institutions. The idea is supported by previous work which uses OWA for making book recommendations; however, priority to the best-ranked university or subject experts is not adequately provided [11]. The issue lies with weight assignment with some of the fuzzy linguistic quantifiers, which generate zero values for the top-ranked institution. Therefore, we have assigned weights so that the higher-ranked universities achieve higher weights compared to lower-ranked universities. The proposed scheme will also save space, as it does not need to record prior preferences, which most of the existing recommender systems do. In addition to this, the proposed approach fills the gap of satisfying new users who do not have any prior logged information and face a cold start issue. The proposed method identifies the inclusiveness in the recommendation process and, hence, provides a better, consensus-driven recommendation. The results of the proposed OWA (most preferred first) are compared with the previous positional aggregation-based scoring (PAS) technique and OWA using other linguistic quantifiers on eight different parameters. The results reveal that the proposed scheme has about 17% improved performance compared to PAS and up to 65% improved performance compared to ordered weighted aggregation, where weights are not assigned according to the order of ranking. In addition, we elaborate on how the OWA can be perceived as human-centric aggregation. It is suggested that the scheme proposed in this study can be very useful in addressing various decision-making problems, especially for recommender systems. In addition, it provides new directions on how various human-specific tasks can be numerically aggregated. To this end, our main contribution lies in presenting a human aggregation approach to inclusively consider human intelligence and a ranking mechanism that helps design a recommendation system capable of providing recommendations to new users and users with some specific requirements through an expert consensus. Section 2 discusses the perspective of human-centric aggregation, including a background to OWA. Section 3 provides an explanation of the proposed scheme for the recommendation of books using OWA operators.