## 4 Discussion  
This study presents a survey explaining existing approaches and techniques used in mitigating a cold start user recommendation using implicit data depicted in the taxonomy (Figure 5). The use of implicit data or indirect method of understanding a new user in order to offer suitable and correct recommendations have always been the best and swiftest approach as opposed to eliciting user preferences explicitly. Furthermore, this study goes on to explain how researchers use these approaches to arrive at and elicit the auxiliary information necessary for cold start user recommendation. The implicit auxiliary information elicited for a cold start user recommendation include behavioral aspects of users such as the studies that used emotions, conversation (critique based) feedback; other user information includes social ties, likes, user reviews, ontology, user demographics, contextual data, tag information, DBpedia data, etc., in order to understand the users' preferences through the users' behavior, context and thereby offer users relevant and accurate recommendation. As cold start recommendation has been notoriously challenging, since the system is deprived of the knowledge or preferences of a new user, researchers have used various clever techniques to attain additional information about the new user implicitly. These approaches involve improving filtering strategies such as collaborative filtering or matrix factorization; or collaborative filtering and matrix factorization coupled with machine learning algorithms (supervised, unsupervised) to form models for training and eventually attaining and eliciting the additional necessary information. Our study has shown various supervised, unsupervised and semi supervised machine learning algorithms coupled with collaborative filtering or matrix factorization used to form models to cold start recommendation. Our study has shown and identified several challenges associated with eliciting auxiliary information for a cold start recommendation. One of the most noticeable challenges from the results is that deriving the auxiliary side information usually involves two distinct processes. This has been a common theme in adapting collaborative filtering and matrix factorization with machine learning algorithms, because eliciting auxiliary information  
Figure 4: Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. involves forming and training the models to obtain the auxiliary information, then incorporating the obtained information into recommendation processes. Leveraging cross domain auxiliary information has always proven to be an effective approach in obtaining accurate and spot-on auxiliary information, however, the knowledge transfer process comes at a cost, such that the auxiliary information from a target domain must be interpreted and carefully linked to align with the source domain recommendation practice. In addition, eliciting auxiliary information from a target domain becomes more challenging when there is discrepancy or too much data or noise in one of the sources or target domains that requires extra effort to transfer the correct cold start user preferences to the source domain. However, some recommendation processes do not specifically involve two distinct processes of eliciting the auxiliary information and then incorporating it into the recommendation process but rather involve the process of converting a cold start recommendation process to a warm start. With this process, few sparse user inferences are obtained, but they are insufficient in making a recommendation; then the second step involves obtaining additional auxiliary information to converting the warm start to generate sufficient information for recommendation. Thus, a cold start recommendation could involve significantly additional amount of time and effort needed compared to a non cold start recommendation.