# Cognitive Similarity-Based Collaborative Filtering Recommendation System
###### Abstract  
This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the _F-measure_ that is a combination of two criteria _Precision_ and _Recall_. Compared with the _Pearson Correlation_, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance. c 20 May 202016 June 202018 June 202020 May 202020 May 202020 May 202020 May  
\(m\) items \(I=\{I_{ij}|j\in[1,...,m]\}\). Each user \(u_{i}\) has a list of item \(I_{u_{i}}\), in which the user has created their feedback. The feedback can be given by the user as a rating score, usually a certain numerical scale, or can be implicitly derived from historical records, by analyzing timing logs, mining web hyperlinks and so on [6,7]. Because, \(I_{u_{i}}\subseteq I\) and it is possible for \(U_{u_{i}}\) to be a \(null\)-\(set\). Therefore, there exists a distinguished user called the active user for whom the task of a collaborative filtering algorithm is to find an item similarity that can be in the two forms, prediction and recommendation. Figure 1 shows the schematic diagram of the collaborative filtering process. In addition, CF algorithms represent the entire matrix \(n\times m\) user-item data with each entry in the matrix \(n\times m\) represents the preference score (ratings) of the user \(u_{n}\) on the item \(i_{m}\). Each rating get a numerical scale and when the user has not rated yet, it will be 0. Generally, collaborative filtering algorithms can be divided into two main categories: memory-based and model-based algorithms [8]. Memory-based Collaborative Filtering algorithm uses all or a sample of the user-item database to do the predictions. Each user is part of a group with similar preferences. The prediction about preferences for the items for users can be created by identifying the neighbors of a new user (or active user). On the other hand, Model-based Collaborative Filtering algorithms allow the system to learn the given model so that the algorithms recognize complex patterns based on training data. Then, based on learned models, the system makes the intelligent predictions for collaborative filtering tasks for the test or real-world data. Besides the outstanding advantages as mentioned, the user-based collaborative filtering still has a lot of successes. However, their widespread use has revealed challenges, such as:  
* **Scalability**: With large systems, such as Netflix ([[https://www.netflix.com](https://www.netflix.com)]([https://www.netflix.com](https://www.netflix.com))) and Amazon ([[https://www.amazon.com/](https://www.amazon.com/)]([https://www.amazon.com/](https://www.amazon.com/))), the number of users and items increases a lot every day. The traditional CF algorithm will face serious scalability issues, with computational resources exceeding actual or acceptable levels. For example, if we have millions of users and millions of distinct items, the complexity of the CF algorithm is already too large.