## 4 Discussion
This study has explained an interesting theme in which most memory-based approaches involve the use of k-nearest neighbor algorithms, for cold start. This involves adapting or modifying these approaches to elicit auxiliary information through tags. This study has found that a cold start recommendation in mobile applications usually involves the use of neighborhood-based approaches. This study has also shown that the use of cross domain data transfers and the use of app data for cross platform mobile apps recommendations have been promising for tackling cold start recommendation in mobile environments. One of the shortcomings and areas of future research in mobile environments is location-based cold start app recommendation because relatively few studies have explicitly focused on location based cold start app recommendation. Furthermore, research is warranted on leveraging contextual information for next app recommendation in mobile app recommendation. Figure 5: A taxonomy of the approaches for deriving and eliciting cold start user implicit data. Our study has shown how social network data and platforms are leveraged in eliciting auxiliary information for cold start users. Some approaches do not literally extract these data explicitly and integrate them into the recommendation process, but rather the information or data from similar users or connected users are studied or considered in forming social network profile for a new user so that a new user could be offered recommendations based on the nature of his/her profile. In this case, auxiliary information does not involve explicitly extracting the auxiliary data/information from the social network, but social network profiles are used as inspirations and references to build cold start user profiles based on similar or connected users as depicted in Figure 2. Arguably, the social network profiles being considered and recognized for the cold start recommendation are the implicit auxiliary information. One of the noticeable findings of this study is that researchers commonly perform a selection or sieving process such as ranking the information or inferences based on certain rules. This is because auxiliary information is arguably not fully reliable and accurate as the information are merely predictions and data from indirect sources. Hence, the information is usually deduced and interpreted by using various techniques; therefore, researchers select and sort these data according to their reliability. The adoption of deep learning approaches has skyrocketed in the past two decades from various research disciplines [98; 99]. The sheer benefits of using deep learning approaches in recommender system has been impressive. This is because deep learning is capable of capturing the non-trivial and non-linear relationships between users and items effectively. Deep learning allows the codification of more complex abstractions as data representations in the higher layers [17], it also handles the complex and sophisticated relationships within data from varied data sources such as with textual, contextual and visual embeddings [17]. Despite all the revolutionary advantages of deep learning in modern computation and in particular recommender systems, relatively few studies have used deep learning approaches for cold start recommendation [47; 92]. It would be interesting to explore deep learning-based models capability of embedding the auxiliary information into a deep feature which would possibly lead to better performance than matrix factorization or traditional filtering strategies. Therefore, further research is warranted in the use of deep learning approaches for cold start recommendations. Figure 5 below depicts the various approaches adopted in eliciting auxiliary information for cold start user recommendation.