## 3 Results
### Building Social Network Profile for Cold Start Users  
Users' relationships in social networks have been exploited to attain auxiliary information for recommendation process. Studies such as [81; 82] have leveraged social network and rating preferences for deriving and eliciting the social relations auxiliary information. However, it is often challenging to accurately exploit the social relations and correctly determine the user preference from both social and rating information for cold start recommendation. For a given user, \"connected users\" are all those users with whom a user is connected to or shares a social relation to, while \"similar users\" are those users who have similar preferences with a particular user. Although, all connected users may not be similar users. In fact, research has shown that the intersection between similar users and connected users is less than 10% [83]. In addition, not all similar users are connected [84]. For collaborative filtering and latent factor model based, there are different ways to utilize social data. Some solutions factorize both the social matrix and the ratings matrix to obtain user's features, while some of them build a rough user profile through analyzing similar implicit users profile data [83]. Researchers have also obtained users missing ratings by aggregating ratings of their similar users [83], thus, these aggregated ratings or inferences of similar users are used as the auxiliary information for cold start recommendation. Although social recommender systems are well suited for the new user cold start problem, there are some noticeable challenges such as a user having large number of social relations which poses challenge in providing cold start recommendation. Secondly, casual relationships for example introduce noise which leads to misleading relations. Furthermore, very few social relations do not also help in providing such cold start recommendation and may cause failure in recommendation. A typical example is the study of that [85] from Table 4 in which social ties of users with their friends are classified. This was done by calculating the affinity strength between users and friends through adapting the coefficient of Jaccard in the social network topology. These data obtained is used as the auxiliary information to offer recommendation of interest to cold start users. One of the main issues with the approach weak personalization, since some users prefer items consumed by weak ties over those by strong ties, other users may prefer in the opposite or different way. A recent study of [86] used social media data to create user profile behavior classification, then used random forest and classification tree algorithm machine learning algorithms to provide recommendation. Although, the study is weak in establishment of relation between user profile and item features. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**S/N**} & \multirow{2}{*}{**Articles**} & **Platform, Data and** & \multirow{2}{*}{**Summary of Approaches**} \\  & & **Domain** & \\ \hline
1 & [27] & Opinions & Collaborative filtering merged with \\  & & & k-nearest neighbors \\ \hline
2 & [85] & DBLP, Ciao, Douban, & The coefficient of Jaccard is adapted as an \\  & & Opinions (Movies, & intrinsic feature of the social network. A \\  & & books, music) & customized ranking model of items are \\  & & & books, music) & formed for generating recommendation to \\ \hline
3 & [86] & Twitter & Leveraging the use of social media (Twitter) \\  & & & data to create a behavioral profile and \\  & & & classify users based on their behavior. \\  & & & Decision tree classifier and random forest \\  & & & machine learning techniques \\ \hline
4 & [87] & Social networks & Geographical distance and social network \\  & & & correlation are leveraged on location based \\  & & & social network LBSNs for building cold start \\  & & & user profile \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Building social network profile for cold start user.