## 3 Results
### Building Social Network Profile for Cold Start Users
The use of implicit social network information has been the most prominent type of implicit data used in matrix factorization-based approaches and has been the dominant and one of the most common approaches adopted by researchers in providing recommendation to new users of a system. Another promising approach used by researchers for providing recommendations to cold start users involve building social network profile for cold start recommendation using social network information. Since there are various types of information available in social networks, this information can be used in different sort of ways to use them especially in tracing users unstated information or preferences. Therefore, leveraging social networks information for alleviating cold start recommendation have proven to be an effective approach due to user's social profile containing various information such as opinions, friends, likes and dislikes background information or demographics, etc. Information and activities that users share on social networks can help define their preferences. Such information includes, for example, friends, favorite sport, favorite movie, educational background, etc., which can be used to directly or indirectly to understand users' preferences as such can be used as auxiliary information cold start recommendation. In a direct way, the system could understand the users' preferred items and therefore recommend similar items. Several studies have shown the richness of leveraging these indirect user data which can help draw reliable and correct inferences about user and consequently understanding users personality (see [88; 90]). Although one of the major downsides of some of these studies such as [88] in Table 4 is that the data used are offline data from a social media platform such as Facebook. It is not the true representation of cold users, which makes it difficult for the system to infer the users' true preferences (see Table 4). The main strengths of this approach is that learned user profile is generic as such, can be used for recommendations across multiple domains [88; 90]. On the other hand, the data are unary (like, dislike) which is a key drawback of the approach. [87] leverage the use of geographical distance and social network correlation is leveraged on LBSNs for user cold start recommendation The approach use social network information on location based social network as auxiliary information for cold start recommendation. This was achieved by forming a geo-social correlation model to extract the social network information of users based on various correlation strength in different geo-social circles. This information is used in building social network user profile based on a cold start user geographical location and context. Forming Users Social Circle  
Eliciting auxiliary information has always been challenging. Researchers have explored various ways to understand a user for offering suitable and preferred recommendation. Friendship plays an important role in recommendation because users' preferences are normally influenced by their circle of friends. Groups of friends or connected friends tend to have similar preferences and therefore neighborhood could easily be formed for a user using social relations such as trust relations, follow relations and group membership. \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 \multirow{4}{*}{5} & \multirow{4}{*}{[88]} & \multirow{4}{*}{Facebook (music, books)} & Proposed three approaches to mitigating, which are: personality-based active learning, personality-based matrix factorization and personality-based cross-domain recommendation \\  & & & Social network data is used with collaborative filtering to exploit community preferences to address cold start recommendation \\ \hline \hline \end{tabular}
\end{table}