# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 2 Literature Review
For instance, in [27] generative adversarial networks, (machine learning frameworks based on neural networks and used in computer vision and natural language processing), are proposed to represent user information by combining the users' demographic information and their preferences. There are also approaches that do not exploit implicit information. In [28] information theoretic measures, based on entropy, are used to tackle the problem of cold users. The measures are used to find a set of products and examine how effective the products are in learning profiles of cold users. The cold-start recommendation problem, where most elements in certain rows or columns of the user-product matrix are 0, can be viewed as a special instance of the sparsity problem, where the user-product matrix can be extremely sparse since both the number of users and the number of products are large. The sparsity problem can be tackled by statistical techniques such as principal component analysis [29]. Empirical studies indicate that dimensionality reduction can improve recommendation quality significantly in some applications but performs poorly in others [30] since potentially useful information might be lost during this reduction process. In [31], instead of reducing the dimension of the user-product matrix (thus, making it less sparse), it is augmented based on transitive interactions between users and products. In addressing the cold-start problem, clustering is used to group the products and/or to group the user profiles into several clusters which in turn are used to provide user/product-content information (e.g., in [32; 33]). Clusters are extracted from the view of ratings, social trust relationships, user/item current context, etc. After such clustering, a new input user (group)--product (group) matrix is derived. The key idea of applying clustering is that traditional collaborative filtering approaches can then be applied to sub-matrices, which alleviates the data sparsity problem to a large extent. In addressing the cold-start problem, ARM is also used to expand user profiles by first extracting a set of association rules for products and then by selecting such rules for each user and for each combination of products (s)he is interested in, whose antecedents much the combination. Finally, the consequences of selected rules are added to the user profile, as in [34; 35; 36; 37; 38]. It is worth noting, that there are also approaches that instead of using association rules, use uncertainty rules and facts [39]. Moreover, combinations of clustering and ARM are used in the literature, in addressing the cold-start problem. One approach consists of applying ARM to expand user profiles and then applying clustering to products based on the expanded input matrix to extract a group of products (e.g., in [40]). Another approach consists of applying clustering to products and then applying ARM to each extracted group of products [41], or to the whole set of groups of products [42]. In addition, another approach concerns the combination of association rules and cluster rules (e.g., in [43]). The proposed methodology first extracts clusters of users (not of products) and then it applies discriminative pattern mining to each group of users. A similar approach is presented in [8], however standard association frequent patterns are used instead of discriminant patterns. Additionally, a similar approach is presented in [44], however, groups of users/items are formed based on the values of a selected user/item characteristic and not based on clustering. In almost all the similar methods, several ratings provided by the users are required (_in-matrix prediction_), whereas the proposed methodology provides valuable recommendations (see Section 4) to cold users that have no rating history at all (_out-of-matrix prediction_).