## 4 Results and Discussions
### Experimental Results
All the related approaches, namely PAS, OWA (at least half), OWA (as many as possible), and OWA (most), have been tested for the same number of books, and all these values have been stored. The OWA with the quantifier'most preferred first' performance for all the books of all the courses concerning eight different parameters is shown in Figure 3.  
Performance Evaluation Mechanism  
The above methods, discussed in the preceding sections, explain how books are recommended for students on a particular subject. The QS ranking [49] is considered to be the base of the complete recommendation process. The books recommended by the top-ranked Indian Institute, according to the QS World University Ranking, are considered for experiments. Several techniques have been applied previously to filter the vast records for recommending the most promising books that have been recommended to readers. These techniques include the PAS technique, OWA (at least half), OWA (as many as possible), and OWA (most). These techniques are compared here with the proposed methodology, which incorporates OWA with modified weight assignment formula. We argue that doing it this way is more human-centric, and we term it as OWA (most preferred first). Since the PAS technique does not involve any weights to be assigned in its process of recommendation, we argue that the process is an unweighted aggregation. The scores are calculated using the PAS technique, and these scores are assigned fuzzy weights using OWA ((at least half), (most), and (as many as possible)). In the proposed scheme, we assign weights to the universities according to their values, which are known by their position in the QS ranking. We claim that in this way, an appropriate recommendation can be made as a priority-based weight assignment is used, which advocates the best-get-best philosophy. However, who will decide which recommendation process is performing well? Or which recommendation process is supposed to be the best? Since there is no such clear protocol to design a recommender system or to judge a recommendation process, it is necessary to evaluate the system, and this evaluation is obviously relative and not absolute. Usually, prediction accuracy is considered the de facto parameter for evaluating recommender systems [51]. It suggests how accurately the recommendation has been made by the adopted approach. It is obvious that the user would prefer a more accurate system. Generally, accuracy is classified as the accuracy of the predictions of the ratings, the accuracy of usage predictions, and the accuracy of the rankings of the items. Since we are advocating in favor of human-centric aggregation, we have taken experts' suggestions for the evaluation of the proposed system. These experts are senior academicians and computer scientists familiar with the Indian education system. We provided the details of the books of the respective courses to them. We adopted an evaluation scheme based on explicit feedback. The experts' feedback on their choice of books was recorded, and the books were ranked. This ranking was then compared with the ranking of the books obtained by the aforementioned schemes. Therefore, the human evaluation by experts would boost the performance evaluation procedure of the adopted recommendation process. The performance evaluation mechanism is presented diagrammatically in Figure 4.