# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 2 Related Works  
### Social-Based Recommender Systems  
Although large numbers of users and items generally participate in an RS, user feedback on items is usually limited. A simple and effective way to improve RS accuracy is to consider information sources such as demographic characteristics [29] or information about friends of users in social networks [30; 31; 32; 33]. Before making decisions, real-world users tend to consider the opinions and suggestions of friends they trust. Hence, social relationships are a possible source of information that can be used to enhance RS performance. [14; 15; 16; 18; 19]. A social media based RS solves the cold-start and sparsity problems by rating the strengths of social relationships and then considering recommendations from friends who have the strongest relationships with the user. Methods of evaluating social information can be categorized as implicit trust-based methods and explicit trust-based methods [6]. Explicit trust, such as a list of trusted userson the Epinions website or the \"friends list\" on Facebook, are demonstrated by the user explicitly. Explicit trust values are values that are explicitly stated by the user. Implicit values are those that are not explicitly stated but are inferred on other evidence, such as user profile. Various methods of exploiting implicit trust information in Collaborative Filtering (CF) systems have already proven effective. For example, the approach described in [30] was based on both user-based and item-based trust information. Implicit trust and global reputation were computed and then used to alleviate the data sparsity problem. Another approach is to use the framework described in [20] to identify implicit trust statements based on the Pareto dominance relations of users. In this recommendations approach, non-prominent users, i.e., those whose opinions are not effective, are excluded from consideration. Based on the small-world property of user communities, ref. [31] presented an algorithm that can derive implicit trust values from user ratings. Implicit trust-based models infer global trust or reputation across the entire user community. Therefore, implicit trust-based models often give inaccurate recommendations when malicious or fraudulent user behaviors are encountered. Thus, explicit trust-based models have attracted interest in recent research. The probabilistic matrix factorization method is proposed in [14], which combines social network with a user-item rating matrix. This method shares the latent feature space of the user to make social recommendations. A user decision is considered a balance between the personal preference of the user and the preferences of trusted friends. Ref. [15] proposed a social trust ensemble method (RSTE) to linearly combine the preferences of the user and the preferences of trusted friends. Another approach used in [32] was to explicitly merge the performance of trusted neighbors and the active user by averaging their ratings, which eliminated the data sparsity problem. In [33], an ant colony optimization algorithm was used in a trust graph to identify a set of users that are like the active user. In [19], a combination of similarity values and trust statements was used to construct a trust network for the active user. A novel reliability measure was used to evaluate the prediction accuracy of the system. Ref. [34] presented a hybrid approach in which both user preferences and social trust are used to make appropriate recommendations. Rating prediction accuracy was improved by considering untrusted relationships as well as propagation effects among users. The authors of [35] proposed an unsupervised learning approach to applying explicit trust in a global reputation model. Their experiments showed that global reputation becomes significant only when the scale of the ego-network of users is small. The level of trust between an active user and the trustees may change over time.