## 2 Background and Related Work
### Fairness  
Regarding fairness, in most cases it refers to omitting or reducing prejudice from a machine learning model or a recommender system [6]. However, recommender systems in most cases provide personalized recommendations, and as a result it is difficult to omit prejudice. Moreover, in most cases recommender systems are used by different stakeholders, and fairness issues may be raised for different stakeholder groups. Therefore, a recommender system, as well as for securing fairness for customers, should also be fair for item providers. For this reason, [PERSON] et al. [6] defined fairness for different stakeholders. Specifically, fairness is divided to customer fairness (C-fairness) and provider fairness (P-fairness). In the current publication, provider fairness implies that the items of each provider are recommended to a similar number of users as items of other providers, and that the users to whom a provider's items are recommended are as diverse as the users that items of other providers are recommended to, and this is the ultimate goal of this study. There are a number of practical applications for addressing provider fairness in recommender systems. For instance, [PERSON] et al. [24] evaluate provider fairness in terms of disparities in relevance, visibility, and exposure for minority groups and propose a treatment that combines observation up-sampling and loss regularization for user-item relevance scores, with satisfactory experimental results. In comparison to the paper at hand, the aforementioned work focuses on fairness across minorities, while the current study approaches all item providers as equal and provides a recommendation strategy that achieves at least a certain amount of average coverage and diversity per item for each provider, if such a strategy exists. On the other hand, the previous study enables the association of an item with more than one provider, which is not the case in the paper at hand, because the problem would be much more complex. Furthermore, [PERSON] et al. [25] acknowledged that individual preferences may limit the ability of an RS to produce fair recommendations. Moreover, they introduced a re ranking approach for fairness-aware recommendations that learns users' preferences across multiple fairness dimensions instead of a single sensitive feature such as race. This approach achieved better experimental results than other approaches in the literature. Unlike our study, the aforementioned approach also focuses on re-ranking of recommended items, which is not the case in this study. Moreover, the aforementioned work pays attention to the diversity of items a user is recommended, while our approach focuses on user diversity for provider items, as well as user coverage of items for each provider. Moreover, [PERSON] et al. [26] acknowledge the provider fairness in terms of geographic imbalance in educational recommender systems. Their study was based on real-world data and observe that data are highly imbalanced in favor of the United States, in terms of open courses and interactions. As traditional RSs tend to reinforce the most represented countries (or providers in general), this study proposes an approach that regulates the share of recommendations for each country and their position in the recommendation list. The definition of fairness in the aforementioned publication is the closest to the definition of the paper at hand, as it recognizes the lack of equity in recommendations across different countries in terms of visibility and exposure, while the paper at hand aims to address the imbalance (lack of equity) in the coverage and diversity of users across the different item providers. Another notable work about fairness in recommender systems is presented in [27]. In this study, [PERSON] et al.