## 3 Results
### Adapting Memory Based Approaches
This approach could be used in a pure cold start item-a new video item with no tag information, then personalized user recommendation for users is generated by exploiting the visual tags. Thus, this promising technique is capable of generating suitable recommendation in both warm and pure cold start recommendation. This has been a promising approach for mitigating cold start in video. Although, this technique was only tested in video recommendation, it would be interesting to explore testing this approach in other similar domains such as music. The study of [63] used k-nearest neighbor to establish a similarity metric for organizing users in the same interest group for movie recommendation. This has been a recurring technique or strategy across cold start recommendation in which users are grouped based on their similar characteristics such that a cold start user is affiliated to a subgroup and offered recommendation based on the features of the sub-group. The data or information from a subgroup is used as auxiliary information for offering recommendation to the cold start user. _D._ _Cold start recommendation in mobile environment:_  
Over the years, there has been a steady increase of research activities in mobile environment. Although, recommendation in mobile environment have largely been researched and recommendations are proposed by the use of contexts information (see [70; 71; 72; 73; 74; 75]), mobile devices and their associated apps require contextual user information to suggest the correct apps based on their location and contexts. Cold start recommendation in mobile environment can arguably be associated with various and diverse set of possibilities for exploiting and obtaining auxiliary information. Due to users' constant dynamics of changing personal, social and location context these instances could be leveraged to determine a more accurate and personalized recommendation to a user bases on users' location, activity, past records in a location, or even emotional state. For instance, a recent study of [76] integrates cloud computing and mobile technology for providing smart market recommendations for context-aware mobile recommendations. This is usually done by cross platform recommendation or culling information from other social network platforms, especially Twitter. Our study has shown that the use of cross domain data transfer and the use of app data for cross platform mobile apps recommendations have been a rich approach for tacking cold start recommendation in mobile environments. From Table 3, it is obvious that research has also employed other techniques of accessing side information to provide cold start users with relevant apps such as the use of privacy data or preferences. There are two types of user cold starts in mobile app which are app cold start that happens as soon as a cold start user install a new app on a mobile device, while the other is user cold start happens when a user installs and opens a cold start (new) on the screen. User cold start is critical because if it is not addressed, it leads to the risk of app abandonment which necessitates offering personalized recommendation from users' interactions with the home screen apps. The advancement of technology and mobile technological devices have made it possible for recommendations to be done based on geographical location, context on mobile devices and mobile applications. Recommending a particular app on mobile devices is becoming increasingly challenging due to the increasing number of apps installed. Consequently, it is important to be able to quickly and accurately predict the next app to be used. Users arguably have difficulty in identifying apps that are relevant to their interests due to the large number of mobile applications (apps) readily at their disposal. Recommender systems that use previous ratings can address such problem (i.e., collaborative filtering) once apps have sufficient ratings from past users.