# Cognitive Similarity-Based Collaborative Filtering Recommendation System
## 3 Cognitive Similarity
### Three-Layered Architecture for Cognitive Similarity  
Our purpose is to extract the similarity between users based on their cognitive similarity in finding the pair of similar items. Then we can use the cognitive similarity to find the k-nearest neighbor of  
Figure 2: Representing the diagram of priority: In the right diagram, the priority of user _[PERSON]_ illustrated by a grey area in ordering five features extracted from the movies (title, genre, director, actor, and plot). Combination with other users, in the left diagram, we can see the related of their priority. active users. Hence, we introduce a three-layered architecture as shown in Figure 3, including (i) an item network \(\mathbb{S}\), (ii) a cognition network \(\mathbb{C}\), and (iii) a user network \(\mathbb{U}\). The networks are considered with several different relations between individuals. Hence, each network is characterized as a set of relations and a set of objects (nodes). The characteristics of each layer and the relationships between layers in the three-layered architecture are described below. * **Item Layer** In the item network \(\mathbb{S}\), nodes are representing items, and relations (i.e., edges) are the similarity between items. A item network \(\mathbb{S}\) is a directed graph \(\langle N_{\mathbb{S}},E_{\mathbb{S}}^{similarity}\rangle\), where \(N_{\mathbb{S}}\) is the set of item and \(E_{\mathbb{S}}^{similarity}\subseteq N_{\mathbb{S}}\times N_{\mathbb{S}}\) is the set of relations between these items. From the item network in the Figure 3, the dot edges represent the relationship between the nodes while these nodes represent the items (the movies). In this study, the relation between items is measured by using cosine vector similarity as mentioned above. * **Cognition Layer** The cognition network \(\mathbb{C}\) is a network \(\langle N_{\mathbb{C}},E_{\mathbb{C}}^{i}\rangle\), in which \(N_{\mathbb{C}}\) is a set of cognitive similarity from groups of user and \(E_{\mathbb{C}}^{i}\subseteq N_{\mathbb{C}}\times N_{\mathbb{C}}\) is the relationship between these groups. The objective relationship from the \(\mathbb{S}\) to the \(\mathbb{O}\) is through the selecting pairs of items similarity by users which can be expressed by a relation: \(Sections\subseteq N_{\mathbb{S}}\times N_{\mathbb{C}}\). We can easily interpret the hubs as being the user's groups that combine a large number of other users with cognitive similarity. These would be an exciting starting point for any new users willing to annotate a similar set of objects as his friend. For example, from the cognition network in Figure 3, the new user \([PERSON]\) has cognitive similarity in groups \(\{[PERSON]\}\) so that \([PERSON]\) will start related with the groups \(\{[PERSON]\}\). These will be enriched during the cognitive of \([PERSON]\) and dynamically changed in this network. The relation of these groups is represented by the overlap from sets of features that collect implicit from the activities of users. In particular, feature sets extracted from movies are \(\{t,g,d,a,p\}\), in which, \(t\) denotes title, \(g\) denotes genre, \(d\) denotes director, \(a\) denotes actor, and \(p\) denotes plot. Likewise, cognitive similarity between users will be extended and imported based on many histories of users' activities (i.e., the item's similarity). Clearly, there is a  
Figure 3: A three-layered architecture for cognitive similarity. difference between cognition networks and item networks though: in item networks, based on several connected items, cognition will be extracted from there. The connection in cognition network extends to include the relation between cognitive similarity of the user and between user groups. Thus, it would be useful to recognize those hubs that connect users on the same groups, these are likely to be the expression of alignment between the two groups. * **User Layer** In the user layer \(\mathbb{U}\), nodes are users, and relations are the numerous kinds of relationships that can be found in cognitive similarity.