## 4 Recommender Systems  
Recommender systems are among the most pervasive applications of algorithmic decision-making in industry, with many services using them to support users in finding products or information that are of potential interest [PERSON] et al. (2016). Such systems find applications in various online platforms such as Netflix, Linkedin, Amazon, etc., where the alternative set of items is much larger which needs to be filtered (and a smaller set of items is to be designed) before being presented to the user. There are various approaches for recommender systems available, such as collaborative filtering [PERSON] et al. (2016), content-based filtering [PERSON] et al. (2016), and knowledge-based recommendation [PERSON] et al. (2016), or some hybrid combinations of these. First, collaborative filtering algorithms are based on the assumption of word-of-mouth, that is,decisions of users are influenced by other users who are closer to her (such as family and friends). User-based collaborative filtering [81] identifies the \(k\)-nearest neighbors of the focal user and based on these nearest neighbors calculates a prediction of the focal user's rating for a specific item. In contrast to user-based collaborative filtering. Item-based collaborative filtering [84] searches for items rated by focal user that received similar ratings as items currently under investigation in order to estimate the probability of its utility. Second, content-based collaborative filtering [82] is based on the assumption of monotonicity of personal interests. In content-based filtering, the content of already consumed items are compared with those of the new items that can potentially be recommended to the user. Based on some \"similarity\" measure of such comparisons, items that are likely to be of interest to the focal user are recommended. Third, knowledge-based recommendation [83] also draws on deeper knowledge (such as semantic knowledge) about the items in addition to ratings and textual item descriptions that the first two approaches use. The study of bias and fairness in recommender systems is an emerging research area that is receiving increasing attention. This is further fueled by evidences of detrimental consequences of popularity bias in recommender systems where recommenders typically emphaize popular items over other \"long-tail\", less popular ones that may only be popular among small groups of users [85]. Notably, a majority of recommender algorithms can be considered as a subset of machine learning algorithms. Notwithstanding, we discuss them separately here due to their unique importance and application pertaining to fairness, and because studying fairness in recommender systems is considered to be challenging and complex as they often consist of multiple models, must balance multiple goals, and are difficult to evaluate due to sparsity and dynamism. Like algorithmic fairness in general, the definition of fairness in recommender systems is as well challenging. In traditional recommender systems, the optimization only takes place on the accuracy of performance, that is, how well the algorithm predicts whether a user will like an item or not based on the utilities of users. Literature in fairness of recommender systems adds in constraints or additional objectives in order to ensure sufficient item coverage, fairness or diversity when it comes to item recommendation. Recommender systems with such constraints can better facilitate their adoption and purchase and fairly deal with the wishes and preferences of all classes/groups of users [86]. Similar to the accuracy-fairness trade-off in machine learning, recommender systems as well suffer from utility-fairness conundrum as making the recommendations fair will likely reduce utility of the entire system. Moreover, recommendation systems also suffer from some unique shortcomings as compared to machine learning fairness in general [87]. For instance, in a recent paper, [PERSON] et al. [88] defined two primary types of bias drawn from imbalance in data.