# Adaptive User Profiling in E-Commerce and Administration of Public Services
## 4 Results and Discussion
### Statistical Analysis of Data  
For the purposes of this article, the statistical program SPSS was used to group, compare and draw conclusions about the quality and reliability of the information produced by the user analysis system. In our sports e-shop, the adaptive profiling system that we created holds information and analyzes and makes predictions regarding the following categories:  
- Sex (Male or Female?) - Parent (Is this user a parent?) Accordingly, variables for the same categories were used for the \"real\" data provided to us through the questionnaires. One hundred adults from all educational levels completed the questionnaires after having made some virtual purchases in our online store. The questionnaire consists of 11 questions, and provides data about respondents from different points of view, such as sex, age, interests, parenthood, education, etc. The selection of these individuals was random. The purpose of this survey was to collect, per user, his/her personal data and his/her interests and to subsequently compare these data with those recorded and predicted by our online profiling system. The results of the survey were very encouraging and showed that our system in most cases worked extremely well. Detailed examples are presented below. More specifically, the questions they were asked to answer were:  
_Question: Which username did you use when you registered?_This question was asked to know exactly which username he/she used when he/she created the account in our system so that we can compare our findings for that specific user. _Question: What is your gender?_  
According to the replies to the questionnaires, 57 were males and 43 were females. Our online profiling system successfully predicted the gender for 84 of those users (47 males and 37 females). This means that the success rate of our system for the gender reached a percentage of 84%. In Table 2, the success rate of the gender prediction is presented. _Question: Are you a parent?_  
Of the participants, 32 replied that they were parents and 68 replied that they were not. Based on the findings of our system, it predicted the correct parenthood for 49 of those users. In Table 3, the success rate of the parenthood prediction is presented. _Question: What are your interests? Choose the ones that interest you (Running, Football, Basketball, Gymnastics, Tennis, Hiking, Swimming, Cycling)_  
In this question, users had the choice to pick any activities that they really like. For each one of these activities and for every user, we analyzed the findings of our profiling system. It turned out that the system worked very well and made accurate predictions. In the following tables the success rates of each activity is presented. In Table 4, the success rate of the Running activity prediction is presented. In Table 5, the success rate of the Football activity prediction is presented. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Running** & \
\begin{tabular}{c} **Real Data from** \\ **Questionnaires** \\ \end{tabular} &
\begin{tabular}{c} **Profiling System** \\ **Accurate Predictions** \\ \end{tabular} & **Success Rate** \\ \hline No & 78 & 63 & 81\% \\ Yes & 22 & 11 & 50\% \\ Total & 100 & 74 & 74\% 1 \\ \hline \hline \end{tabular} \({}^{1}\) The success rate of our system for the running activity is 74%.  
\end{table} 
Table 4: Running activity. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & \
\begin{tabular}{c} **Real Data from** \\ **Questionnaires** \\ \end{tabular} &
\begin{tabular}{c} **Profiling System** \\ **Accurate Predictions** \\ \end{tabular} & **Success Rate** \\ \hline Male & 57 & 47 & 82\% \\ Female & 43 & 37 & 86\% \\ Total & 100 & 84 & 84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gender analysis.