Collaborative filtering has been a dominant approach in the recommender systems community since the early 1990s. Collaborative filtering (and other) algorithms, however, have been predominantly evaluated by aggregating results across users or user groups. These performance averages hide large disparities: an algorithm may perform very well for some users (or groups) and poorly for others. We show that performance variation is large and systematic. In experiments on three large-scale datasets and using an array of collaborative filtering algorithms, we demonstrate large performance disparities across algorithms, datasets and metrics for different users. We then show that two key features that characterize users, their mean taste similarity and dispersion in taste similarity with other users, can systematically explain performance variation better than previously identified features. We use these two features to visualize algorithm performance for different users and we point out that this mapping can capture different categories of users that have been proposed before. Our results demonstrate an extensive mainstream-taste bias in collaborative filtering algorithms, which implies a fundamental fairness limitation that needs to be mitigated.

People’s ability to influence others’ opinion on matters of taste varies greatly—both offline and in recommender systems. What are the mechanisms underlying these striking differences? Using the weighted k-nearest neighbors algorithm (k-nn) to represent an array of social learning strategies, we show—leveraging methods from network science—how the k-nn algorithm gives rise to networks of social influence in six real-world domains of taste. We show three novel results that apply both to offline advice taking and online recommender settings. First, influential individuals have mainstream tastes and high dispersion in their taste similarity with others. Second, the fewer people an individual or algorithm consults (i.e., the lower k is) or the larger the weight placed on the opinions of more similar others, the smaller the group of people with substantial influence. Third, the influence networks emerging from deploying the k-nn algorithm are hierarchically organized. Our results shed new light on classic empirical findings in communication and network science and can help improve the understanding of social influence offline and online.