# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 2 Literature Review  
The approaches proposed in the literature, addressing the cold-start recommendation problem by exploiting implicit information, can be broadly grouped into three categories, i.e., the similarity-based models, matrix factorization models, and feature mapping models [4]. Similarity-based models aim at predicting user-product matrix for the cold users by assigning a cold user to a group, based on usually applying machine learning techniques to both implicit and explicit information and then on detecting similar warm users. Matrix factorization and feature mapping models factorize the user-product matrix into two latent representations. Then, one can learn a latent representation using the Implicit information, and then use it to predict the user-product matrix for the cold users. For instance, a similarity-based model presented in [15], where a generalized matrix algebra framework is defined using generalized matrix multiplication permitting any similarity metric operators, e.g., inner product and cosine similarity. This framework is applied to a matrix of user-product purchases where the purchased products are viewed as user attributes and extends this matrix with non-product personal attributes P, e.g., demographics, Facebook friends, or page likes. As another example, in [16] classification is used to assign a new user to a specific group and then an intelligent technique detects the \"neighbors\" of the new user. In [17] a deep neural network extracts the content features of items and a modified, already existing, CF model takes these features into the prediction of ratings for cold-start items. Bi-clustering has also been used in the cold-start recommendation problem. In [18], the recommendation approach consists of three phases, the filtering, the bi-clustering, and the prediction phase. In the filtering phase, a confidence level is employed to remove trivial ratings, i.e., ratings considered unimportant to a user, and popular products and frequent raters are kept to the user-product matrix. Then, in the bi-clustering phase, users and products are clustered simultaneously and a smoothing strategy is used to eliminate data sparsity and diversity of users' styles. In the prediction phase, the method seeks like-minded users (within the same cluster) and similar products (within the same cluster) to the ones under observation and combines the two predictions into one. An example of the matrix factorization model is presented in [19], and it is based on a factored representation of the product-product similarity matrix. The latter is learned as the product of two low-dimensional latent factor matrices. In [20] a low-rank linear auto encoder is proposed which consists of an encoder that maps the user behavior space (e.g., a user-product matrix) into a user attribute space (side information), and a decoder that reconstructs the user behavior by the user attribute. In [21] the authors propose hybrid recommender models that use content-based filtering and latent Dirichlet allocation (LDA)-based models. There are many more techniques that deal with the cold-start problem by combining collaborative filtering with content-based methods, including those using simultaneous co-clustering [22] and self-organizing maps. Some works focus on the extraction of implicit information from the user's social networking activity [23; 24]. In [25], the authors exploit data from social media to classify users' profiles and then make predictions with the use of machine learning methods, especially decision trees and random decision forests. In [26] the authors try to make a cross-site cold-start product recommendation. They use neural networks for users' and products' feature representations to transform them into user embeddings and then retrieve them in cold-start situations through a feature-based matrix factorization technique. There are also approaches presented in the literature, which are not based on matrix operations or on a projection into latent spaces.