## 1 Introduction  
Recommender systems in large numbers are in operation today. These systems are based on diverse techniques, approaches, and are used to provide recommendations in various disciplines and application domains. The development of these recommender systems, as well as their improvement for various domains and purposes has given rise to an active area of scientific research [1]. This development and evolvement of recommender systems is based on the continuing evolution of artificial intelligence, information retrieval, machine learning, statistical methods, data mining, etc. [2]. Recommender systems are highly successful on e-commerce web sites capable of recommending the products that customers would probably like. Recommender systems are software tools that recommend users to items or items of interest to users based on their preferences [3]. These systems used either implicit or explicit data/information to determine users' preferences. Explicit information involves data collection from surveys or questionnaire to understand users' preferences, while implicit information retrieval is basically an indirect method which involves culling information from online platforms such as purchasing history, demographics, likes and other traces of users' online activity. Recommender systems have boosted the existence and success of several application domains such as e-commerce, online entertainment, tourism industry, etc., in which customers are recommended and become more attractive to these products or materials due to the accuracy of recommendation offered by these software systems. Recommender systems use algorithms; these algorithms are typically based on filtering strategies that utilize the available information about items or users to provide recommendations. Content based and collaborative filtering are the two most commonly used filtering strategies [3]. Content based is based on the analogy that if a user prefers an item, then the user would most likely prefer similar items of similar characteristics. There are two main collaborative filtering approaches: latent factor model and neighborhood-based approaches [4; 5]. The neighborhood-based approach is based on recommending items to a group of users sharing similar characteristics with the notion that users having similar characteristics would most likely have similar preferences. In this approach, a product or item is selected for recommendation if one neighborhood member is determined for a user and an item liked by the neighbors is selected for recommendation. On the other hand, the latent factor model-based approach attempts to determine factors that help in understanding users' psychology and personalities to determine the suitable item for recommendation. Both these filtering strategies train models based on prior information which are used for the item recommendation usually in the form of a ratings matrix which contains the ratings given by different users to various items. Moreover, collaborative filtering exhibit better performance and ease of use compared to content-based filtering [3; 4; 5; 6]. Although, combining both content-based and collaborative strategies as a hybrid form have been a promising approach in affording the strengths of both filtering strategies [3; 7; 8]. When a new user interacts with a recommender system or a new item is added to the repository of a recommender system, the system faces a challenge in giving recommendations due to the unavailability of prior information about the user or the item; this problem is called cold start problem [9; 10; 11] in recommender systems. There are two main types of cold start problems in recommender systems: new user cold start problem and new item cold start problem. In the new user cold start problem, the recommender system faces a problem in offering recommendations when the new user is introduced to the system as it has no prior information about the user.