# Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
## 1 Introduction  
Recommender systems aim to forecast a user's rank, interests, and preferences in specific products (or services) and make corresponding recommendations to the user for purchase. The recommender systems forecast product purchases from a very large inventory of products based on the user's past purchase behavior and possibly on the user's current context. The demand for recommender systems has arisen because of the information surplus created through the huge volume of data. Recommender systems can be broadly divided into collaborative filtering-based, content-based, and hybrid systems [1]. Content-based filtering (CBF) exploits product characteristics and user demographics to recommend similar products to the ones that the user purchased in the past. Collaborative filtering (CF) is the most popular approach, and it utilizes the opinions of other users with similar preferences, given that similarities can be computed from the historical transactions of the user under observation. Collaborative filtering can be divided into two categories, i.e., memory-based and model-based. Memory-based or user-based takes into consideration previous user product ratings to recommend products to a user for purchase based on similarity measures and correlation. Model-based collaborative filtering predicts rates of products to be recommended to the user through the development of a model with the use of machine learning techniques such as clustering, neural networks, decision trees, etc. In the rest of the paper, we use the term product to also denote services and information items. In collaborative filtering, the user's past purchase behavior consists of the user's feedback which can be either implicit or explicit [2]. Implicit feedback is usually binary and it is obtained by observing the user's behavior (products purchased or not, songs listened or skipped, web pages browsed or not, content downloaded or not, etc.) [3]. Explicitfeedback is the discrete ratings assigned by the user for every product purchased. Users and products are divided into warm (users whose previous product purchase behavior is known or products for which user interactions are available) and cold (users whose previous product purchase behavior is unknown, e.g., new users or products for which user interactions are not available, e.g., new products). Recommendations concerning the latter constitute the popularly known cold-start recommendation problem. When CF faces the cold-start problem, it is difficult to provide useful recommendations to the user, since no prior ratings exist. Therefore, content-based filtering (CBF) is used instead to provide recommendations. However, CBF algorithms recommend products based on similar content. This leads to recommendations that are less diverse with each other. The latter poses various problems, e.g., when a user considers one recommendation as useless, it is highly possible that all the rest similar recommendations might be considered worthless as well. In this paper, we address the cold-start problem in recommender systems by applying both clustering and association rule mining (ARM) techniques. More specifically, the proposed methodology is based on discriminative itemset mining technique that extracts discriminant frequent patterns for each different cluster of old users extracted by a clustering technique. To our knowledge, the proposed methodology is the first one that exploits discriminative itemset mining to address the cold-start problem for both its _in-matrix prediction_ and _out-of-matrix prediction_ setup. _In-matrix prediction_ refers to users that purchased at least one product (or to products that have been rated by at least one user). The proposed methodology addresses also the _out-of-matrix prediction_, where new users have never carried out any purchase before (or products that have never been rated before). Thus, the proposed methodology tackles a more challenging problem that is not often studied in the literature [4] and fills this research gap.