# Machine Learning Based Recommendation System for Web-Search Learning
## 4 Results and Discussion
### Recommender Model Analysis  
The core operations of information logging, information processing, and word mapping, as defined in algorithm 1, played a role in understanding the learner's knowledge acquisition through the simulation and providing better measures. The percentage of the measures increases after the learning phase is completed, as defined in Algorithm 1. The cluster-based recommender model, as defined in Algorithm 2, suggests the top \(N\) better suggestions for the learners after the information has been processed using Algorithm 1.  
The proposed recommender is analyzed based on the following metrics:  
\[recall=items\ count\ of\ valid\ target\ learner/I_{User} \tag{3}\]  
\[precision=items\ count\ of\ valid\ target\ learner/|L| \tag{4}\]  
\[ranking\ score=\sum_{i\ \in\ I_{User}}rank\ (i)/|I_{User}| \tag{5}\]  
\[RMSE=\sqrt{\frac{\sum_{i=0}^{n}(p\ (i,\ u)-rate(i,\ u))}{n}} \tag{6}\]  
The proposed recommender resource processing is evaluated, and the metrics precision, ranking score, and recall are calculated using Equations (3)-(6) for mean \(\upmu\) and standard deviation \(\sigma\); these are tabulated in Tables 5 and 6, respectively. The proposed recommender is compared with other methods, such as CF, MDHS, and UPDD [23, 24, 25, 26, 27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Concept Groups** & **Text** & **Videos** & **Overlapping Text or Video** & **Overall** \\ \hline CG1 & 15 & 13 & 8 & 18 \\ CG2 & 7 & 7 & 5 & 12 \\ CG3 & 8 & 8 & 5 & 10 \\ CG4 & 9 & 8 & 4 & 10 \\ CG5 & 12 & 11 & 3 & 12 \\ CG6 & 15 & 14 & 4 & 16 \\ CG7 & 10 & 9 & 3 & 12 \\ CG8 & 12 & 12 & 3 & 12 \\ CG9 & 15 & 14 & 3 & 16 \\ CG10 & 18 & 17 & 4 & 20 \\ CG11 & 20 & 20 & 3 & 25 \\ CG12 & 15 & 15 & 5 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean number of words from Q2 retraced to specific text, or video transcripts [7, 12, 13, 14, 17, 18, 19, 20].  
Figure 6: Count of not stemmed words, scored concepts and scored concept groups.  
It has been found that better measures are obtained using the proposed recommender compared to other methods. The RMSE measures for different learner group sizes = 250, 300, 350, 400, 450, and 500 are sketched in Figure 7. The RMSE lies between (8%, 16%) when the learner group size is less than 500, and the maximum value of RMSE is 21% when the learner group size reaches 1500.  
The size of the recommendation list versus the precision measure is plotted in Figure 8. The proposed model achieves a precision of 27% when the size of \(L\) becomes 100. The precision and recall measures for different learner sizes = 250, 500, 750, 1000, 1250, and 1500 are plotted in Figure 9, and the proposed recommender obtains better results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Strategies** & **Ranking Score** & **Recall** & **Precision** \\ \hline CF & 0.005 & 0.004 & 0.023 \\ MDHS & 0.003 & 0.007 & 0.036 \\ UPOD & 0.002 & 0.006 & 0.027 \\ Proposed Method & **0.001** & **0.008** & 0.015 \\ \hline \end{tabular}
\end{table}
Table 6: \(\sigma\) comparison with other methods [23; 24; 25; 26; 27; 32; 23].  
Figure 7: RMSE for different learner group sizes.