# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 5 The Prototype
### Results and Discussion
#### 5.4.4 Discussion  
For achieving a good recommendation system, we include implicit context preference based on Algorithm 2 in the learning and recommendation process, since when rule items repository increases, so does accuracy. That algorithm was proposed with the hypothesis that the users cover all situation rules, and then enriches his agenda with new relevant situation rule for daily living not already doing it. With that hypothesis, our system gives mainly relevant with close matched context ignoring other possible contextual configurations.  
The comparative analysis of recommendation approaches used for situation rules recommendation reveals the following important points:  
* Among the four recommendation criteria, the proposed approach-using user's implicit and explicit preferences, user's history, user's current situation gives highest accuracy for rule item recommendation. However, explicit preference-based approach gives lowest accuracy.
* In addition, the proposed approach shows highest level of performance with 100% smart domains covered under the different user's domains (security, demotic, health, study, work, tourism and shopping). The explicit preference-based approach shows lowest area covered under poor user-specified preferences.
* Among the four approaches, explicit preference-based approach presents the fastest algorithm for rule item recommendation with the highest prediction speed and lowest training time. However, the proposed approach presents best recommendation approach with the highest accuracy and recall rates and acceptable execution time.  
The proposed approach on context-aware preferences and rules items contents attains promising accuracy results as compared to traditional machine learning approaches as showed in Figure 14 and Table 7. However, the semantic-based context-aware probabilistic approach is much better than traditional machine learning approaches in managing and recommending relevant rules in terms of accuracy, recall and F1-score. The response time of our approach on daily life rules dataset is demonstrated in Table 8 towards the recommendation of relevant rules from three user's locations (home, office and outdoors). It is semantically flexible in situation enrichment and adaptation model for context-aware pervasive applications. However, the rules recommendation time must be improved in large datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multicolumn{3}{c}{**Number of Mobile Devices**} \\ \cline{2-4}  & **10** & **50** & **100** \\ \hline Explicit Preferences & 29.40 & 33.02 & 46.05 \\ Implicit Preferences (item content) & 25.20 & 36.74 & 61.18 \\ Explicit and Implicit Preferences & 42.32 & 51.37 & 84.19 \\ Contextual Bayesian (context situation) & 148.56 & 153.08 & 193.27 \\ Preferences and Contextual Bayesian & 177.04 & 182.24 & 245.14 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of response time (ms) of proposed approach using different criteria (number of situation rules is fixed to 80).  
In our future work, will consider the optimum execution time of the large number of user situations datasets from the real-world daily life situations while considering the collaborative mechanisms and high-performance machine, which makes the results more practical.