# Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria
## 4 Results and Discussion
These graphs show that our proposed chunking method represents the baseline technique UCF_VF2E and the red line represents our proposed technique with chunking. In each of the three graphs, UCF_Chunking outperformed the UCF_VF2E. Similarly, our proposed methods of recommendations with chunking performed better than the other respective baseline recommendation techniques of VF2E in each graph. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Number of Recommendations**} \\ \cline{2-9}  & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** \\ \hline UCF\_VF2E & 5.86 & 7.79 & 12.45 & 15.05 & 17.84 & 20.68 & 24.36 & 26.57 \\ UCF\_Chunking & 6.02 & 14.15 & 17.75 & 20.52 & 23.34 & 27.15 & 30.32 & 33.6 \\ ICF\_VF2E & 3.57 & 6.47 & 9.61 & 12.63 & 17.67 & 22.72 & 25.06 & 28.87 \\ ICF\_Chunking & 5.72 & 8.86 & 11.76 & 17.24 & 21.05 & 24.07 & 28.32 & 30.78 \\ CBF\_VF2E & 3.73 & 6.88 & 9.4 & 11.48 & 13.37 & 14.83 & 15.78 & 16.97 \\ CBF\_Chunking & 8.75 & 14.34 & 17.47 & 22.08 & 25.09 & 27.75 & 31.28 & 34.69 \\ Hybrid\_VF2E & 4.41 & 7.86 & 13.09 & 15.54 & 19.47 & 20.69 & 23.37 & 27.44 \\ Hybrid\_Chunking & 10.68 & 15.76 & 19.66 & 23.59 & 28.78 & 33.16 & 34.53 & 36.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of baseline methods and proposed method. In Figures 8-11, our proposed method of keyword extraction with chunking outperformed all other respective baseline methods of VF2E. We evaluated the performance of our method on three different groups of five users for each recommendation technique and gave the comparison of baseline methods in the same way. These graphs were just the separation of the above-combined graphs given in Figure 7 for better understanding and to represent the recommendation techniques' performance. This shows the performance comparison of the average results of three groups. It is shown in all graphs mentioned that performance of the hybrid recommendation technique was better than all other recommendation methods, whether those are baseline methods or the chunking itself. Moreover, we calculated the average of three groups of five users to combine all our results into one graph. Comparison among our proposed techniques (Chunking) with baseline methods (VF2E) on average results of three groups is given in Figure 12.  
The overall performance of the recommendation technique with the proposed method of chunking in keyword extraction performed better than all other techniques. The lines red, green, light blue, and light green in the graph represent the recommendation techniques with chunking-based keywords extraction method, which are UCF_Chunkin, ICF_Chunking, CBF_Chunking, and Hybrid_Chunking, respectively. The base article could achieve the maximum score of 31% on 25 recommendations with a hybrid recommendation. Therefore, we concluded that this technique performed better than all other recommendation techniques. We also showed in our experiments that the hybrid recommendation results were always better than the other techniques, achieving scores of 22.22%, 34.48%, 32%, 36.84%, 41.18% with 50 recommendations for five individual users, respectively. Using the proposed chunking-based keyword extraction method, the average score was 33.34%. In comparison, they achieved a 30% score for 50 recommendations. The score of recommendations is increased as the number of recommendations increases. In each figure shown above, the x label is the number of recommendations on average, representing the average scores of each set of three graphs. Figure 7: Comparison among our proposed techniques (Chunking) with baseline methods (VF2E) for a group of three users in each graph (combined). The red, green, blue, and light green lines show the performance score of our proposed methods UCF_Chunking, ICF_Chunking, CBF_Chunking, and Hybrid_Chunking methods over the various baseline methods of VF2E.