## 4 Results and Discussions
### Discussion  
The comparison is made based on eight different parameters. These parameters, with their details, have been mentioned in Section 4.1. In addition, four parameters are used as veracity measures, and another four are used as fallacy measures. The values of the veracity measures are shown in Figure 5. It is very clear from the graph that OWA (most preferred first) has outperformed the other techniques for all these parameters. The OWA with at least half quantifier, however, has the same P@10 and MAP as that obtained by OWA (most preferred first). In contrast, the value of the modified Spearman's correlation coefficient and mean reciprocal rank is better for OWA (most preferred first) than OWA (at least half).  
The improvement in the performance of OWA (most preferred first) for Mean Absolute Error (MAE) with respect to OWA (at least half), (as many as possible) and (most) are 6.66%, 28.20%, and 23.28%, respectively. In contrast, with respect to PAS, there is an improvement of 8.94%. For Root Mean Square Error (RMSE), 14.64% improved results have been achieved with respect to PAS, whereas, while comparing OWA (most preferred first) with OWA (at least half), (as many as possible) and (most), it is remarkable that percentages of improvements are 4.24%, 33.46%, and 24.55%, respectively (Figure 6). In addition to these results, we can say that with the help of ranked weights, the errors can be reduced, which leads to more accurate results.  
Figure 4: Performance evaluation scheme for the book recommendation approach.  
Figure 5: Comparison of related recommendation approaches for veracity measures including P@10, MAP, MRR, and modified Spearman’s rank correlation coefficient.  
In Figure 7, we can easily notice that the FPR@10 and FNR@10 both have the same values for all the approaches. This is because we have a complete list. For a partial list, these two parameters may also have different values. Interestingly, the result for OWA (at least half) is the same as OWA (most preferred first), and these results are on par with other operators. The false rate is 36% reduced with respect to OWA (as many as possible) and 17.85% improved than PAS. These improvements clearly indicate how powerful OWA can be when treated according to human-centric situations and how closely they are related to human perception and aggregation.  
This suggests that these quantifiers can be useful in many areas of application, especially in recommender systems. Furthermore, human-centric aggregation does not need any prior information about user activity. Thus, it enhances the approach by reducing time and saving storage space that is usually required to acquire this knowledge.  
Further, with the help of OWA and the modified weight assignment formula for OWA (most preferred first), it is observed that human-centric terms, such as intention, intelligence, judgment, and vision, have been incorporated into this work. Each step in the procedure is based upon human perception, which we argue is human-centric. After all, the primary objective of the paper is to apply human-centric aggregation. Initially, the intention of the ranked universities is taken by involving their recommended books, and then the books recommended by experts are considered, which keeps the vision of those experts. Next, performance evaluation shows the judgment, and finally, what we recommend is classified  
Figure 6: Comparison of related recommendation approaches for RMSE and MAE.  
Figure 7: Comparison of related recommendation approaches for FPR@10 and FNR@10.  
as intelligence. Thus, different aspects of human perceptions have been assimilated. The concept is diagrammatically demonstrated in Figure 8.