# Cognitive Similarity-Based Collaborative Filtering Recommendation System
## 2 Related Work  
GroupLens [3; 6] has implemented the MovieLens ([[https://movielens.org](https://movielens.org)]([https://movielens.org](https://movielens.org))) [16] as one of the large systems that allow new users to sign up and rate their favorite movies. GroupLens researchers have also released a data set that they collected over the years with more than 25 million movie ratings. They provide a pseudonymous collaborative filtering solution for movies based on their data set in order to improve and solve the disadvantages of collaborative filtering, especially to improve user-based collaborative filtering in a recommender system. Other technologies have also been applied such as Bayesian networks and clustering. A Bayesian Network (BN) [17; 18; 19] is a compact representation of a multivariate statistical distribution function. BN encodes the probability density function governing a set of random variables \(\{X_{i}|I\in[1,...,n]\}\) by specifying a set of conditional independence statements together with a set of conditional probability functions. In particular, a BN consists of a qualitative part, a directed acyclic graph where the nodes mirror the random variables \(X_{i}\) and a quantitative part, the set of conditional probability functions. In general, BN creates a model based on a _training-set_ with a decision tree at each node and edges representing user information. The model can be built off-line over a matter of hours a day. The resulting model is very small, fast, and essentially as accurate as the nearest neighbor method. Recently, a lot of improvements for user-based CF have been proposed to mitigate the effects of the data sparseness [20; 21]. For example, a singular vector decomposition was used to condense the original user-item matrix [22] for dimensional reduction, and latent semantic models [23] was used to cluster the users and items. However, these approaches have a disadvantage that the decomposition must be renewed every time another user or rating is added to the matrix. Another, more recent contribution is based on an analysis of prediction errors to improve the accuracy of user-based CF. This approach has the limitation that the cost for the calculation of errors [24] of all ratings during training is quite an expensive. Alternative approaches, using recursive prediction strategies, have been proposed to exploit not only the neighbors but also the neighbors of the neighbors [25]. Because the similarity calculation of neighbors of all neighbors is required, such strategies incur high computational costs and grow exponentially with the depth of the recursion. Besides, these strategies must be enrich the information of the user-item matrix to improve the performance of user-based CF [8; 26]. In addition, in [27; 28], the two item-based similarity measures have been designed to overcome the cold-start problem by incorporating genre data of items. They use popular datasets such as MovieLens and MovieTweets in their experiments. According to their approach, an item be uniform to other items because they have more than one common genre. Therefore, by considering the association of common genres, they exploit one of the similarity measures that is determining the degree of direct asymmetric correlation between items. The proposed method in this paper was inspired by [29] that proposes the Rated-Item Pools (called RIP-based) approach to improve user-based CF. This approach aims to eliminate extra calculations that increase computational complexity and thereby avoid the need to add external knowledge resources resulting in potential cost. In order to formulate the approach, the author used a related method [3] that applied Equation (1) to predicting the rating value \(R_{u,i}\) for an active user \(u\) and item \(i\)  
\[R_{u,i}=R_{u}+\sum_{v\in N_{u,i}}\frac{sim(u,v)}{\sum_{j\in N_{u,i}}sim(u,j)}*( R_{v,i}-R_{v}), \tag{1}\]  
where \(N_{u,i}\) represents a subset of the neighbors \(v\) of the active user \(u\) who explicitly rated item \(i\).