## 1 Introduction
In the new item cold start problem, the recommender system has no ratings for a newly added item, so it faces difficulty in recommending the right item for a user, or the right user for a particular item. Between these two types of cold start problems, the new user cold start problem has been more widely studied due to the challenge of accurately recommending an item to a new user [9; 12]. Similarly, there are also two types of cold start problem in terms of data sparsity: Pure cold start, and warm start. Pure cold start or cold start refers to a situation in which there are no available ratings or past information about a user or item, while warm start refers to a situation whereby the available ratings, information or preference is sparse and is insufficient or could not be used to provide recommendation. However, researchers have used several terminologies such as \"complete cold start\" or \"pure cold start\" to indicate the complete absence of ratings; or the use of \"incomplete cold start\" or \"warm start\" to indicate the existence of very little information or preference for recommendation (typically rating by a single user). Over the years, researchers in various domains have leverage the use of implicit data in addressing new user cold start problems. Researchers have devised the use elicit implicit data from various sources such as social network streams like Twitter, Facebook, background information, etc., to provide new user with better recommendations. One of the most common approaches to mitigate cold start for user is by adapting or coupling the traditional collaborative filtering techniques with other techniques most notably machine learning algorithms. Although there have been influential studies that reviewed and studied cold start problems in recommender systems, these studies have a limited scope in explaining the state of recommending items to new user in the recommender system domains. For example, the study of [13] explains and reviews the literature on the use of social network data to mitigate cold start problem. The comparative study of [14] categories cold start literature based on the use of additional data sources; selection of the most prominent groups of analogous users; and enhancement of prediction using hybrid methods; the study of [15] reviews sparsity issues in recommender systems. In addition, other key influential studies in recommender systems such as [16] have only focused on reviewing cross domain recommendation by identifying the most widely used cross-domain recommender systems building block definitions, algorithms adopted, challenges, common features between them. In addition, due to the significant attention and popularity of deep learning in several research fields for its effectiveness in information retrieval, recommender systems researchers and practitioners have progressively adopted deep learning approaches in recommending suitable items to users. For instance, the study of [17] reviews recent research efforts on deep learning-based recommender systems. Despite complementary or auxiliary information being the integral information needed for a cold start recommendation, recommender system literature lacks a detailed explanation of the process of reaching and obtaining such auxiliary information typically by integrating or coupling various filtering strategies with supporting machine learning and other algorithmic approaches. This study is entirely different from previous related studies as it focuses on explaining and providing an insight into the detailed process of reaching and extracting the auxiliary information needed for cold start recommendation. This study also aims to explain the various approaches and techniques employed by researchers in offering recommendations to cold start users, using various algorithms and techniques; the various application domains for cold start recommendation and identifying areas for future research.