# Cognitive Similarity-Based Collaborative Filtering Recommendation System
## 5 Experimental Results
### Evaluation  
To evaluate the recommender system, firstly, the pair of item similarity (in this evaluation, it will be called item) of each user was divided into two sets. These datasets were selected randomly and called the \(training\)-\(set\) (the first set) and the \(Test\)-\(set\) (the second set). The proposed algorithms were first implemented on the \(training\)-\(set\) in order to filter N items to be recommended to the active user that is called \(top\)-\(N\). Then, the items in \(top\)-\(N\) were compared with the items in the \(Test\)-\(set\). The common items in the \(Test\)-\(set\) and \(top\)-\(N\) were called \(Hit\)-\(set\). Finally, after obtaining the \(Test\)-\(set\), \(training\)-\(set\), and \(Hit\)-\(set\), we can calculate the accuracy percentage of the algorithm using evaluation criteria. Here, we used two criteria evaluation that are \(Precision\) and \(Recall\). Measuring the \(Precision\) will returns the proportion of relevant recommendations according to the total recommendations (denotes as \(N\)), where the relevant recommendations are the ratings equal to or greater than a threshold. The \(Recall\) is the proportion of relevant recommendations regarding the total relevant items (from the total number of items selected by the user). However, note that whereas \(N\) is a constant, the number of relevant items is not. Hence, the \(Recall\) is a \"\(relative\)\" measure because extract relevant recommendations from a few relevant items are more difficult than a large of relevant items. Generally, for better performance, we use the \(F_{1}\) that is a combination of two above criteria and can be formulated as follows:  
\[F_{1  
that the accuracy of collaborative filtering based on cognitive similarity (CF-Cognitive Similarity) is higher than collaborative filtering based on Pearson correlation similarity (CF-Pearson Correlation Similarity) approach. The proposed method achieves improvement over the baseline in the best case is 11.1%. Consider various values of _top-N_ in a set \(\{10,50,100,150,200,250\}\), we have the comparison between the proposed method and the baseline as shown in Table 2.  
In addition, to improve the evaluation, we continue to enable a comparative analysis by using MAE and RMSE as the evaluation metric and the measurement is defined as follows:  
\[\mathbf{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-y_{i}^{p}|, \tag{10}\]  
\[\mathbf{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-y_{i}^{p})^{2}}, \tag{11}\]  
where \(n\) denotes the number of cognitive similarity values in the _Test-set_, \(y_{i}\) denotes the actual cognitive similarity values, and \(y_{i}^{p}\) denotes predicted cognitive similarity values. In general MAE can range from 0 to infinity, where infinity is the maximum error according to the cognitive similarity values scale of the measured. The main reason following this approach is because the predicted cognitive similarity values can create the ordering of items in which the predictive accuracy can be used to measure the ability of a recommendation system to rank items according to user cognitive [34]. In order to create the _Test-set_, we divide the items of each user following the k number of section/folds (k-fold cross-validation) where each fold is used as the _Test-set_ at some point. According to [35], we decided k is set as 5 because yield test error rate estimates that suffer neither from excessively high bias nor very high variance. Specifically, the data set was split into five folds. The first fold used as the _Test-set_ and the remainder used as _Training-set_ at the first interaction. In the next interaction, the second fold will be a _Test-set_ and the remainder is _Training-set_. This process will repeat until each fold of the five folds has been used as the _Test-set_. As mentioned above, we used Pearson Correlation Similarity which uses the similarity and recommendation models (1) and (2) as a baseline.