S ITIOS N ATURALES S AGRADOS COMO ESTRATEGIA ARGUMENTATIVA

To present the results of the proposed user grouping and content classification techniques, we used the data of first 25 days. The data of first 25 days contains approximately equal number of videos with the data utilized in 5-fold cross validation.

According to the proposed technique in Section 4.3, a hierarchical tree was constructed for our set of users. As explained in section 4.3, we need to set a lower bound for the number of users in each group. We considered 100 as the minimum acceptable number of users in each group to penalize the creation of the small groups in the clustering process.

We used MATLAB to construct hierarchical tree for our set of users. To link the users (objects), we utilized "complete"3approach.

After that, the total entropy for different cutting levels in [0, Lmax] was calculated. The

results show a global minima when the maximum number of clusters equals 7.

To introduce a measure for testing how distinct the resulted groups are, a vector, called representer vector, is assigned to each group. Representer vector, denoted by ⃗R, of each group is defined as the average of the ⃗RVs of users in the group. Angles between the representer vectors can be interpreted as a measure of distinction between the groups. If this angle is close to 90 degrees, it can be concluded that there is a perfect distinction between two groups. Equation (4.32) defines the angles between representer vectors of group i and group j(denoted by ⃗Riand ⃗Rj, respectively).

γi j = cos−1   ⃗ R_i· ⃗R_j    ⃗ Ri       ⃗ Rj     . (4.32)

For the obtained 7 groups, Fig. 4.3 shows the box plot of these angles. Each box represents the range of the angles between the representer vectors of different groups. The median of these angles, except for group 6, is always more than 70 degrees. This indicates a good distinction between groups. Since angles between representer vector of group 7 and other groups range all more than 80 degrees, this group shows the best separation from other groups. In contrast, group 6 has relatively small distinction from other groups. This is due to the point that the set of videos, that have been requested by users of group 6, have relatively large overlap with the sets of videos requested by users of other groups. As the result, a large range of the angles between representer vector of group 6 and other groups is observed.

3_{In the most basic level, the complete approach pairs an object to its nearest object. Then, the approach}

considers the furthest distance between objects of two previously paired objects as the distance between that two pairs.

Fig. 4.3 Angles between representer vectors of different groups

The elements of representer vectors estimate the amount of interest to each video; so, one can utilize them to identify the videos that have attracted a large number of users in each group. Considering this, the set of the most popular videos for each user group have been extracted. By analyzing the constructed set of most popular videos, we guess age group of each user group4. Table 4.1 presents the number of users, set of most popular videos and the age group of the user groups.

According to table 4.1, group #5 contains the largest number of users. “The Apprentice” and “EastEnders” (two high popular programmes in the UK) are the most popular videos to the users of this group. Analyzing the set of the most popular videos reveals that group #5 are interested mostly in the programmes prepared for adult people. Group #7 is the second largest group that comprises 2164 users. Most of the users of this group are interested in “The white Queen”. Five remaining groups contain users who are mostly interested in the programmes prepared for children. As can be seen, most popular videos in group #6 intersects with the most popular videos of group #3 (users in both groups are interested in “The Sarah Jane Adventure”). Due to previously observed range of angles between representer vector of group #6 and representer vectors of other groups, this intersection can be expected.

It is worth mentioning that all the above-discussed results have been obtained from investigating the output of the proposed unsupervised grouping technique. Groups can be separated to adult users and children by a quick investigation of the most popular videos in each group. Additionally, deeper investigations can result in more useful information the characteristics of users. This information have application in marketing industry. The information can be utilized to target advertisements to the most relevant audiences.

Table 4.1 Groups’ characteristics

Group number Number of users Most popular videos Video age group

Group #1 130 League of Super Evil Child

Group #2 576 Arthur Child

Group #3 281 The Sarah Jane Adventure,

The Story of Tracy Beaker Child Group #4 845 The Story of Tracy Beaker Child

Group #5 19343 The Apprentice,

EastEnders Adult

Group #6 281 The Sarah Jane Adventure,

Tracy Beaker returns Child

Group #7 2164 The white Queen Adult