## Literature Review
### Recommendation Techniques  
Recommendation techniques have been proposed in the social network world by a multitude of papers. They are so many that it is impossible to report them all. Hereafter, we report those that we consider representative of most recent developments.  
The Reference [29] proposed a Recommendation System for Podcast (RSPOD). The system recommends podcasts, i.e., audios, to listen to. The system utilizes the intimacy between social network users, i.e., how well they virtually communicate with each other. RSPOD works (i) by crawling podcast information, (ii) by extracting data from social network services and (iii) by applying a recommendation module for podcasts.  
To predict user's rating for several items, [30] considers social trust and user influence. In fact, it is argued that social trust and influence of users can play a vital role to overcome the negative impact on the quality of recommendation caused by sparsity of data. The phenomenon of social trust is based on the sociology theory called \"Six Degrees of Separation\" [31]: the authors proposed a framework that jointly adopts user similarity, trust and influence, by balancing preferences of users, trust between them and ratings from influential users for recommending shops, hotels and other services. The proposed framework was applied on a data set collected from dianping.com, a Chinese platform that allows users to rate the aforementioned services.  
According to [PERSON] et al. [32], previous recommendation systems mainly focus on recommendations based on users' preference and overlook the significance of users' attention. Influence of trust relation dwells more on users' attention rather than users' preference. Therefore, an item of a user's interest can be skipped if it does not get his attention. To counter this, they proposed a probabilistic model called Hierarchical Trust-Based Poisson Factorization, which utilizes both users' attention and preferences for social recommendation of movies, music, software, television shows and so on.  
Similarly, [33] aimed at accurately predicting users' preferences and relevant products recommendation on social networks by integrating interaction, trust relationships and popularity of products. The key focus of the proposed model is on performing analysis of users' interaction behavior to infer users' latent interaction relationships, based on product ratings and comments. Moreover, the popularity of product is considered as well, to help support decision making for purchasing products.  
By emphasizing on the importance of social interaction on recommendation systems [34], presented an approach based on mapping the weighted social interaction for representing interactions among users of a social network, by including historical information about users' behavior. This information is further mined by using an algorithm called Complete Path Mining, which helps find similar social neighbors possessing similar tastes as of the target user. To predict the final ratings of unrated items (such as software, music, movie and so on), the proposed model uses social similar tendencies of the users on complete paths.  
To summarize, the reader can see that recommendation techniques are thought to recommend single items (such as posts, podcasts or products) to users, based on the existing relationships among users. [PERSON] et al. [35] address the same general problem that we envision in our application scenario, i.e., recommending users to follow: they propose a framework to recommend the 50 users that are more similar to a specific user; they jointly exploit user features (such as ID, gender, region, job, education and so on) and user relationships. In contrast, in our envisioned scenario, we propose a different approach, i.e., recommending other users to establish a relationship with (e.g., to follow) on the basis topics their posts talk about. At the best of our knowledge, this problem has not been addressed yet in literature.